Ethics and artificial intelligence

Inglada Galiana, L.; Corral Gudino, L.; Miramontes González, P.

doi:10.1016/j.rceng.2024.02.003

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Inglada Galiana, L. Corral Gudino, P. Miramontes González" "autores" => array:3 [ 0 => array:4 [ "nombre" => "L." "apellidos" => "Inglada Galiana" "email" => array:2 [ 0 => "ingladagaliana0@gmail.com" 1 => "lingladaga@saludcastillayleon.es" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "L." "apellidos" => "Corral Gudino" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "P." 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The increasingly influential presence of AI in various spheres of human life raises critical questions about the nature and application of our ethical norms.Ethics, traditionally framed in human terms, is confronting unprecedented challenges and opportunities in the AI era. As these technologies become more integrated into our lives, they become active agents that can influence or even make critical decisions that were previously the exclusive domain of human beings. This evolution makes it necessary for us to reconsider and redefine what is meant by liability, privacy, autonomy, and justice in an increasingly digitized, automated context. Ethics and AI are deeply interconnected in a bidirectional manner and AI capabilities and applications beget new ethical issues. In addition, we consider that AI has the potential to significantly transform all aspects of society and human life, from health to security to the economy. This requires a careful ethical analysis to guide its development. Adopting a proactive ethical approach to AI development is crucial to ensuring that this technology advances in a way that benefits society as a whole and it is used responsibly and ethically, respecting human rights and fostering a harmonious coexistence between humans and intelligent machines.Key questionsThere are two questions we must propose to advance in the reformulation of ethical and moral norms: how is AI reshaping our perceptions and the applications of ethics and morality? And, are our current ethical theories equipped to address the challenges posed by AI?<a class="elsevierStyleCrossRefs" href="#bib0005">1–5</a> Next, we showcase these dilemmas and the evolution of AI to date.Evolution of artificial intelligenceHistory and development of AIIn this context, we must talk about the history of AI. It is a fascinating narrative of human aspirations, technological innovations, and scientific discoveries. Its theoretical origins go back to the first conceptualizations of intelligent machines in literature and science, including pioneers such as Alan Turing and his famous Turing test.<a class="elsevierStyleCrossRef" href="#bib0030">6</a> In the 1950s and 1960s, its initial development began with the exploration of the first practical uses of AI, such as chess programs and the first attempts at natural language processing. The golden age and 'AI winter' took place in the 1970s and 1980s, when the initial optimism gave way to a decline in interest and funding due to the technological limitations of that era. Since the end of the 1990s, advances in machine learning algorithms and a greater availability of data and computational power led to an AI renaissance, a resurgence accompanied by machine learning. In the current era, there is advanced AI that has the most recent developments such as deep learning, neural networks, and AI systems like GPT-4.<a class="elsevierStyleCrossRefs" href="#bib0035">7–11</a>Impact on various sectorsModern AI is transforming multiple industries, redefining processes, and creating new possibilities. AI is having a significant impact in the field of security, both in terms of national security and cybersecurity. This impact ranges from improved surveillance and reconnaissance capabilities to defense against cybersecurity threats. AI offers improved capabilities to protect nations and individuals from a variety of threats. The use of AI in this field must be carefully managed to ensure respect for ethics and human rights and to avoid creating new vulnerabilities. Moreover, AI is redefining the financial sector by offering opportunities to improve efficiency, decision making, and the personalization of services. However, it also brings with it complexities in terms of risk management and data privacy. To fully capitalize on the benefits of AI in finance, it is crucial to address these challenges through appropriate regulations, transparency, and a thorough understanding of the technology’s capabilities and limitations. Collaboration among financial institutions, regulators, and technology experts will be key to forging a financial future that is innovative, secure, and ethical.In addition, in terms of predictive analyses, AI is able to predict potential risks by analyzing market trends and past behavior. This is particularly useful in the early identification of credit, market, and operational risks. Likewise, AI algorithms can detect suspicious activity that could indicate fraud, money laundering, or breach of financial regulations, thus improving security and regulatory compliance. Financial technology (FinTech) startups are using AI to innovate in areas such as payments, lending, and insurance, challenging traditional financial institutions. Finally, AI’s use of personal and financial data raises important concerns in terms of data privacy and security.<a class="elsevierStyleCrossRefs" href="#bib0060">12–20</a>Basic ethical principalsTo what degree are people dependent on virtual assistants or recommendation systems? It is crucial to respect patients’ ability and right to make voluntary, informed decisions about their medical care. The impact on human decision making must be analyzed by examining how AI influences or dictates human decisions in different contexts, from daily life to critical situations. Thus, how might this limit or change the autonomous decision-making process?The autonomy of machines must also be analyzed by examining the degree to which machines that use AI have “autonomy” and the ethical challenges this poses. To what extent should machines make independent decisions, especially in critical contexts such as autonomous driving or medical care?The existence of beneficence is also very important, because it constitutes the commitment to act in the patient’s best interest, maximizing benefits and minimizing harm. As well as the duty not to cause harm, which is essential in the Hippocratic oath of “first, do no harm.” We must also respect the equity in the distribution of medical resources and the treatment of patients, ensuring that everyone is treated fairly and equitably.The challenges in assigning liability among developers, users, and AI itself must be discussed. This analysis could include discussing existing laws and regulations and how they may need to be adapted to address these issues. It is mandatory to address the issue of who is liable when an AI makes an error or causes harm, exploring scenarios such as autonomous vehicle accidents or medical diagnosis errors made by AI.Finally, it is necessary to reflect on how AI affects individual privacy, especially in the collection and analysis of large amounts of personal data. Examples include surveillance using AI and the use of personal data for targeted advertising. In addition, the challenges and effects of AI on privacy at a group or societal level, such as the potential for extensive social profiling and the monitoring of groups or communities, should be considered.“Explainability”The concept of “explainability” is crucial it is gaining significant attention in both AI development and in ethical discussions. Explainability refers to the ability of an AI system to explain its decisions, processes, and actions in a way that is understandable to humans. It bridges the gap between advanced technological capabilities and human-centered operations, ensuring that AI systems are not only powerful and efficient, but are also in line with ethical standards, regulatory requirements, and societal expectations. As AI continues to evolve, explainability will play a crucial role in fostering trust, responsibility, and transparency in AI systems.<a class="elsevierStyleCrossRefs" href="#bib0105">21–30</a>Trust and transparencyFor users to trust and interact effectively with AI systems, they need to understand how these systems make decisions. This is especially vital in high-risk areas such as healthcare, finance, and legal systems. Explainability ensures transparency in AI operations, allowing users to see what data the AI system uses and how it processes those data to make a decision. When AI systems can explain their decisions, it becomes easier to determine the liability for these decisions, which is particularly important when a decision leads to negative consequences.On the other hand, explainable AI allows for better ethical oversight, as the reasons behind decisions can be examined in terms of equity, bias, and alignment with societal values. Users can interact more effectively with AI systems if they understand the rationale behind the results and this understanding can lead to better integration of AI into everyday tasks. In addition, explainability allows users to provide more accurate feedback on AI decisions, facilitating the continuous improvement of the AI system.Regulations around AI increasingly require transparency and liability. Explainability can help in compliance with these legal requirements and standards. In addition, as international standards for AI are developed, explainability will likely become a key component, ensuring that AI systems can be used and trusted globally.<a class="elsevierStyleCrossRefs" href="#bib0155">31–35</a>Facilitation of reliable AIExplainability is a cornerstone of ethical AI development, as it ensures that AI systems are in line with human values and ethical principles. Likewise, it is necessary to move away from opaque AI systems to others in which the decision-making process is clear and understandable.Some AI models, particularly deep learning systems, are inherently complex and not easily interpretable. Sometimes, the most accurate AI models are the least explainable and vice versa. There must be a balance between performance and explainability. In regard to the development of universal standards, there is no single approach to explainability; what’s more, what is sufficient in one context may not be sufficient in another.<a class="elsevierStyleCrossRefs" href="#bib0180">36–38</a>Different ethical theories in regard to artificial intelligenceThis ethical theory focuses on maximizing happiness or general welfare. In the context of AI, a utilitarian approach would evaluate AI actions and decisions according to their ability to maximize collective welfare. As for its application in AI, AI systems could be designed with the aim of maximizing general welfare, but the challenge of defining and measuring said “welfare” and balancing the interests of different groups then arise.On the other hand, the theory of deontology is based on compliance with ethical standards and duties. From this perspective, actions are ethically correct if they adhere to a set of established rules. Its application in AI would involve programming AI to follow predefined ethical rules. However, the rigidity of deontology may not be suitable for all the complex situations that AI could face.The virtue ethics focuses on the moral characteristics and virtues of the moral agent, rather than on rules or consequences. As for its application in AI, it would seek to translate into developing AI that exhibits or promotes qualities such as empathy or justice. However, programming these “virtues” into an AI is a significant challenge.Ethics of care also exist. This emphasizes the importance of relationships and mutual care, bringing interconnectedness and dependence to the foreground. As for its application in AI, you could seek to develop AI systems that prioritize and foster caring and supportive relationships, especially in areas such as health or education.On the other hand, pragmatism focuses on the practical consequences and adaptability of ethical standards in different contexts. In regard to its application in AI, this theory would suggest developing AI that is capable of ethically adapting to different situations, which poses challenges in terms of programming and autonomous decision making.The theory of justice is based on the principles of equity, justice, and rights. It emphasizes equality and the equitable distribution of resources and opportunities. In terms of its application in AI, it would involve ensuring that AI systems operate in a fair and equitable manner without discriminatory bias and that their development and use do not increase inequality.Finally, the relational ethics considers that ethical decisions must be based on the context of relationships and community. Its application in AI would focus on how AI affects human relationships and social dynamics, seeking to minimize negative impacts and promote collective benefits.Each of these theories offers unique perspectives on how ethics should be addressed in the development and application of AI. The choice of one theory over another may depend on the specific context, the aims of the AI system, and the priorities of developers and users. Given that AI affects a wide range of areas, a hybrid, flexible ethical approach that can adapt to different situations and challenges is likely needed.<a class="elsevierStyleCrossRefs" href="#bib0195">39–44</a>Future of ethics in AIThe possibility that future developments in AI will lead to systems that can display forms of consciousness or advanced autonomy has profound ethical and philosophical implications. Reflecting on these possibilities involves exploring fundamental questions about the nature of consciousness, identity, and ethics in relation to non-human entities. Some of these issues are analyzed below.The nature of consciousness in AIOne of the fundamental challenges is to define what exactly “consciousness” means in the context of AI. Can AI truly be conscious in the same sense as humans are or would its “consciousness” be something fundamentally different? If it is accepted that AI can be conscious, this leads to ethical questions about its rights, its treatment, and respect for its subjective “experience.”Advanced autonomyAdvanced autonomy in AI refers to the ability to make complex decisions independently. This raises questions about liability and accountability, especially in critical or high-risk situations. As AI becomes more autonomous, the issue arises of how to maintain control over these technologies and ensure that their decisions are aligned with human values and norms.<a class="elsevierStyleCrossRefs" href="#bib0225">45–49</a>Ethical implicationsIf AI is considered conscious or highly autonomous, should it have rights? How would these rights be defined and protected? The emergence of conscious or autonomous AI could fundamentally transform the way humans interact with machines, defying our current conceptions of the relationship between humans and technology.Rights of AIAs AI systems become more advanced and autonomous, the question of whether they should be considered entities with rights arises. This includes questions about whether AI can or should have any kind of legal status or “personhood.” If AI systems are granted rights, questions about liability also arise. How do you hold an AI system accountable for its actions and decisions? In addition, how do you balance its autonomy with public safety and welfare?Advanced AI securityAs AI becomes more autonomous and capable, the potential for misuse or malfunction also increases. This could have serious consequences for public and global security. Establishing frameworks to control and regulate AI is essential in order to prevent abuse and ensure that the technology is used safely and ethically. This includes safeguards against autonomous decision making in critical areas such as defense and national security.AI in global decision makingAI has the potential to significantly influence global politics and the economy through its ability to do tasks that range from automating data analysis to predicting economic and political trends. In addition, AI can play a role in decision making in areas such as climate change, resource management, and diplomacy. This raises ethical questions about representativeness and the inclusion of diverse human perspectives in such decisions.<a class="elsevierStyleCrossRefs" href="#bib0250">50–53</a>Regulation and legislationA legal framework must be developed that specifically addresses the unique challenges posed by AI, including issues of liability, intellectual property rights, and data use. It is also necessary to foster the creation of international regulations on AI, especially in areas in which the technology transcends national borders, such as cybersecurity and data privacy.<a class="elsevierStyleCrossRefs" href="#bib0270">54,55</a>Medical ethics and artificial intelligenceAI has enormous potential in medicine, but it also faces great challenges. There is a wide range of applications for AI in medicine that will require a variety of authors, editors, and reviewers with knowledge of AI. Experience in AI is linked to commercial applications and thus, transparency about potential conflicts of interest will be necessary. Furthermore, in medicine, AI must be subjected to rigorous studies, like in other fields, though there are challenges such as misalignment between the development and application data.<a class="elsevierStyleCrossRefs" href="#bib0280">56,57</a>As AI becomes a more common tool in healthcare, its ethical implications will become increasingly critical. Advances such as those described in Moore’s law allowed for an exponential increase in storage capacity and computational power. This made the development of machine learning possible, with applications such as reading medical images and early detection of disease outbreaks. Even so, there are still challenges to address, such as the standardization of clinical research with AI, the control of bias in data, privacy protection, and the definition of ethical standards. Chatbots can be useful as an aid in documentation, but it is necessary to validate the accuracy of their responses. AI will improve medical professionals’ work by freeing up time for patient care, but it will require joint adaptation between professionals and these tools.<a class="elsevierStyleCrossRefs" href="#bib0290">58,59</a>AI has enabled advances in infectious disease surveillance through early warning systems, pathogen classification, risk assessment, identification of focal points of contagion, and epidemiological surveillance. It has allowed for the creation of HealthMap for the early detection of outbreaks, AI models to automate microbiological readings, and the detection of antimicrobial resistance. There are also reinforcement learning applications that optimally assign COVID-19 tests and the EDS-HAT system to track hospital outbreaks. However, challenges include data quality and representativeness, privacy concerns, and the inability of AI to replace international cooperation. AI improves surveillance, but requires a rigorous methodology that controls for bias and critically evaluates the generalization of the models. Its successful implementation depends on global cooperation.<a class="elsevierStyleCrossRef" href="#bib0300">60</a> Lastly, AI is revolutionizing various aspects of the health field, including medical diagnostics, treatment personalization, and pharmaceutical research. This work will now explore how AI is impacting these three areas.Medical diagnosticsAI is being used to detect diseases in early stages, significantly improving the chances of successful treatment. For example, AI algorithms in radiology can identify subtle signs of diseases such as cancer that might go unnoticed by the human eye.<a class="elsevierStyleCrossRef" href="#bib0305">61</a> In addition, AI is also revolutionizing the analysis of medical images such as X-rays, magnetic resonance imaging (MRI) tests, and computed tomography (CT) scans, providing faster and more accurate diagnoses. <a class="elsevierStyleCrossRef" href="#bib0310">62</a>Treatment personalizationAI allows for more personalized medicine, tailoring treatments to each patient’s individual characteristics. This includes considering the patient’s genetics, environment, and lifestyle to formulate more effective treatments. Likewise, AI systems can help monitor and manage chronic diseases, adjusting treatments in real time based on the patient’s response.Pharmaceutical researchAI is accelerating the discovery and development of new drugs, rapidly analyzing vast amounts of data to identify potential compounds and predict their efficacy and safety.<a class="elsevierStyleCrossRef" href="#bib0305">61</a> It can also help in optimizing clinical trials in aspects that range from candidate selection to monitoring outcomes and side effects.<a class="elsevierStyleCrossRef" href="#bib0315">63</a>Specific examplesSome AI algorithms are used to detect breast, skin, and other cancers based on medical images, often with greater accuracy than traditional methods. In addition, smartphone applications that use AI to monitor vital signs and symptoms provide personalized analysis and recommendations.Autonomy and informed consentA major challenge is ensuring that patients understand how AI will influence their diagnosis and treatment. This is crucial for genuine informed consent. AI should be used as a tool to supporting—not replacing—shared decision making between the physician and the patient, respecting patient autonomy.Privacy and data confidentialityHealth data are extremely sensitive. AI in medicine requires robust safeguards to protect these data against unauthorized access and security breaches. Patients must have control over how their data are used and it is essential to obtain consent for the use of their data in the training and application of AI models.Quality and equity in careAI algorithms can perpetuate inherent biases in the training data. It is crucial to develop strategies to identify and mitigate these biases to avoid disparities in healthcare. In addition, it must be guaranteed that advances in AI do not create a divide in medical care in which only certain groups have access to the best possible care.Liability and decision makingIn cases of diagnostic or treatment errors involving AI, it is essential to determine who is liable. Is it the AI developer, the medical professional who uses it, or both?AI systems must be transparent and their decision-making processes must be explainable so that medical professionals can trust and understand their recommendations.Challenges in research and developmentAll AI development and research in medicine must be done in an ethical manner, respecting the rights of participants and prioritizing their welfare. Collaboration among experts in technology, ethics, law, and medicine is vital. What’s more, it is necessary to train health professionals on the ethical and practical aspects of AI.The accuracy of AI when preparing for internal medicine exams, such as the boards in the United States of America, is notable due to several factors.<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005">•Analysis of data and patterns: AI can process and analyze large volumes of medical data, identifying patterns and trends that may be difficult for humans to discern. This is particularly useful in test preparation, in which understanding patterns in diagnoses and treatment can be key.</li><li class="elsevierStyleListItem" id="lsti0010">•Personalization of learning: AI can adapt the study materials to students’ individual needs. By analyzing previous responses and progress in studying, AI can identify weak areas and strengthen them with additional resources.</li><li class="elsevierStyleListItem" id="lsti0015">•Continuous content updating: Given that the field of medicine is constantly evolving, AI can keep up with the latest research and clinical guidelines, ensuring that students study the most up-to-date, relevant information.</li><li class="elsevierStyleListItem" id="lsti0020">•Mock exams and assessments: AI can generate practice exams that mimic the format and style of real boards. This not only helps students become familiar with the exam structure, but also allows them to assess their preparation effectively.</li><li class="elsevierStyleListItem" id="lsti0025">•Instant feedback and performance analysis: AI systems can provide immediate feedback on practice questions, allowing students to understand and correct errors instantly. In addition, they can analyze general performance to offer specific study recommendations.</li><li class="elsevierStyleListItem" id="lsti0030">•Integration of multiple sources of knowledge: This leads to a more holistic, comprehensive approach to studying.</li><li class="elsevierStyleListItem" id="lsti0035">•Reduction in bias and human error: by relying on objective data and algorithms, AI can help reduce the personal biases and errors that often occur in traditional studying.</li></ul>However, it is crucial to remember that AI is a tool for support and does not substitute human clinical judgment or the practical experience needed in internal medicine. Its most effective use is in addition to traditional study techniques and clinical training.<a class="elsevierStyleCrossRefs" href="#bib0320">64–68</a>There are different AI Chatbots from different companies, such as Google's Bard, Anthropic's Claude, Microsoft Copilot, and most recently, Google’s Gemini. Each has its own characteristics. In the case of ChatGPT, developed by OpenAI, the chronology of the emergence and updating of its different versions is as follows:<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0040">•GPT-3: Introduced in June 2020, GPT-3 represented a significant leap in terms of size and complexity, with 175 billion documents. Its ability to generate coherent text and its versatility on a variety of tasks made it a benchmark in natural language AI.</li><li class="elsevierStyleListItem" id="lsti0045">•ChatGPT and GPT-3.5: ChatGPT, based on a modified version of GPT-3.5, was launched at the end of 2022. It incorporated a reinforced learning capability with human feedback, improving the relevance and accuracy of responses.</li><li class="elsevierStyleListItem" id="lsti0050">•GPT-4: This is the latest version, updated in April 2023. It features significant improvements in comprehension, text generation, and multitasking capabilities compared to its predecessors. It is probably the most powerful chatbot at present. In February 2024, another large amount of data will be dumped.</li></ul>Historically, general-purpose technologies have been slow to deliver on their promises, a phenomenon known as the “information technology productivity paradox.” The successful implementation of new technologies in healthcare is particularly challenging. However, generative AI has unique properties that could shorten the usual delay between implementation and gains in productivity and/or quality. The healthcare ecosystem has evolved to be more receptive to generative AI. What's more, many healthcare organizations are ready to implement the necessary innovations in culture, leadership, workforce, and workflow.The ability of generative AI to rapidly improve and the ability of organizations to implement additional innovations indicate that generative AI could deliver significant improvements in healthcare faster than previous technologies have. Likewise, previous healthcare technologies, such as electronic medical records, have followed a pattern similar to the productivity paradox, offering mixed results in terms of efficiency. Finally, although the implementation of generative AI faces unique challenges in healthcare, its distinctive characteristics and the recent evolution of the healthcare ecosystem offer a promising path forward for overcoming these challenges and achieving significant improvements.<a class="elsevierStyleCrossRefs" href="#bib0345">69,70</a>FundingNo funding was received for this article.Conflicts of interestThere are no conflicts of interest." "textoCompletoSecciones" => array:1 [ "secciones" => array:17 [ 0 => array:3 [ "identificador" => "xres2103849" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1792957" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres2103848" "titulo" => "Resumen" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0010" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1792956" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "Key questions" ] 6 => array:3 [ "identificador" => "sec0015" "titulo" => "Evolution of artificial intelligence" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0020" "titulo" => "History and development of AI" ] 1 => array:2 [ "identificador" => "sec0025" "titulo" => "Impact on various sectors" ] ] ] 7 => array:2 [ "identificador" => "sec0030" "titulo" => "Basic ethical principals" ] 8 => array:2 [ "identificador" => "sec0035" "titulo" => "“Explainability”" ] 9 => array:2 [ "identificador" => "sec0040" "titulo" => "Trust and transparency" ] 10 => array:2 [ "identificador" => "sec0045" "titulo" => "Facilitation of reliable AI" ] 11 => array:2 [ "identificador" => "sec0050" "titulo" => "Different ethical theories in regard to artificial intelligence" ] 12 => array:3 [ "identificador" => "sec0055" "titulo" => "Future of ethics in AI" "secciones" => array:7 [ 0 => array:2 [ "identificador" => "sec0060" "titulo" => "The nature of consciousness in AI" ] 1 => array:2 [ "identificador" => "sec0065" "titulo" => "Advanced autonomy" ] 2 => array:2 [ "identificador" => "sec0070" "titulo" => "Ethical implications" ] 3 => array:2 [ "identificador" => "sec0075" "titulo" => "Rights of AI" ] 4 => array:2 [ "identificador" => "sec0080" "titulo" => "Advanced AI security" ] 5 => array:2 [ "identificador" => "sec0085" "titulo" => "AI in global decision making" ] 6 => array:2 [ "identificador" => "sec0090" "titulo" => "Regulation and legislation" ] ] ] 13 => array:3 [ "identificador" => "sec0095" "titulo" => "Medical ethics and artificial intelligence" "secciones" => array:9 [ 0 => array:2 [ "identificador" => "sec0100" "titulo" => "Medical diagnostics" ] 1 => array:2 [ "identificador" => "sec0105" "titulo" => "Treatment personalization" ] 2 => array:2 [ "identificador" => "sec0110" "titulo" => "Pharmaceutical research" ] 3 => array:2 [ "identificador" => "sec0115" "titulo" => "Specific examples" ] 4 => array:2 [ "identificador" => "sec0120" "titulo" => "Autonomy and informed consent" ] 5 => array:2 [ "identificador" => "sec0125" "titulo" => "Privacy and data confidentiality" ] 6 => array:2 [ "identificador" => "sec0130" "titulo" => "Quality and equity in care" ] 7 => array:2 [ "identificador" => "sec0135" "titulo" => "Liability and decision making" ] 8 => array:2 [ "identificador" => "sec0140" "titulo" => "Challenges in research and development" ] ] ] 14 => array:2 [ "identificador" => "sec0145" "titulo" => "Funding" ] 15 => array:2 [ "identificador" => "sec0150" "titulo" => "Conflicts of interest" ] 16 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-12-21" "fechaAceptado" => "2024-01-10" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1792957" "palabras" => array:3 [ 0 => "Ethics" 1 => "Artificial intelligence" 2 => "Explainability" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1792956" "palabras" => array:3 [ 0 => "Ética" 1 => "Inteligencia artificial" 2 => "Explicabilidad" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics in medical artificial intelligence (AI) involves ensuring that technologies are safe, fair, and respect patient privacy. This includes concerns about the accuracy of diagnoses provided by artificial intelligence, fairness in patient treatment, and protection of personal health data.Advances in artificial intelligence can significantly improve healthcare, from more accurate diagnoses to personalized treatments. However, it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration, involving healthcare professionals, artificial intelligence experts, patients, and ethics specialists to guide and oversee their implementation.Finally, transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental." ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "La relación entre ética e inteligencia artificial en medicina es un tema crucial y complejo y se encuadra en su contexto más amplio. Así, la ética en inteligencia artificial (IA) médica implica asegurar que las tecnologías sean seguras, justas y respeten la privacidad de los pacientes. Esto incluye preocuparse sobre la precisión de los diagnósticos proporcionados por la IA, la equidad en el tratamiento de pacientes y la protección de los datos personales de salud.Los avances en inteligencia artificial pueden mejorar significativamente la atención médica, desde diagnósticos más precisos hasta tratamientos personalizados. Sin embargo, es esencial que los desarrollos en inteligencia artificial médica se realicen con una consideración ética fuerte, involucrando a los pacientes, profesionales de la salud e inteligencia artificial y especialistas en ética para guiar y supervisar su implementación.Por último, es fundamental la transparencia en los algoritmos de inteligencia artificial y la formación continua para los profesionales médicos." ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:70 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "A unified framework of five principles for AI in society" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "L. Floridi" 1 => "J. 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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Artificial intelligence &#40;AI&#41; is not only a facet of advanced technology&#44; but also a catalyst for profound ethical and moral transformations&#46; The increasingly influential presence of AI in various spheres of human life raises critical questions about the nature and application of our ethical norms&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">Ethics&#44; traditionally framed in human terms&#44; is confronting unprecedented challenges and opportunities in the AI era&#46; As these technologies become more integrated into our lives&#44; they become active agents that can influence or even make critical decisions that were previously the exclusive domain of human beings&#46; This evolution makes it necessary for us to reconsider and redefine what is meant by liability&#44; privacy&#44; autonomy&#44; and justice in an increasingly digitized&#44; automated context&#46; Ethics and AI are deeply interconnected in a bidirectional manner and AI capabilities and applications beget new ethical issues&#46; In addition&#44; we consider that AI has the potential to significantly transform all aspects of society and human life&#44; from health to security to the economy&#46; This requires a careful ethical analysis to guide its development&#46; Adopting a proactive ethical approach to AI development is crucial to ensuring that this technology advances in a way that benefits society as a whole and it is used responsibly and ethically&#44; respecting human rights and fostering a harmonious coexistence between humans and intelligent machines&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Key questions</span><p id="par0015" class="elsevierStylePara elsevierViewall">There are two questions we must propose to advance in the reformulation of ethical and moral norms&#58; how is AI reshaping our perceptions and the applications of ethics and morality&#63; And&#44; are our current ethical theories equipped to address the challenges posed by AI&#63;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#8211;5</span></a> Next&#44; we showcase these dilemmas and the evolution of AI to date&#46;</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Evolution of artificial intelligence</span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">History and development of AI</span><p id="par0020" class="elsevierStylePara elsevierViewall">In this context&#44; we must talk about the history of AI&#46; It is a fascinating narrative of human aspirations&#44; technological innovations&#44; and scientific discoveries&#46; Its theoretical origins go back to the first conceptualizations of intelligent machines in literature and science&#44; including pioneers such as Alan Turing and his famous Turing test&#46;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> In the 1950s and 1960s&#44; its initial development began with the exploration of the first practical uses of AI&#44; such as chess programs and the first attempts at natural language processing&#46; The golden age and &#39;AI winter&#39; took place in the 1970s and 1980s&#44; when the initial optimism gave way to a decline in interest and funding due to the technological limitations of that era&#46; Since the end of the 1990s&#44; advances in machine learning algorithms and a greater availability of data and computational power led to an AI renaissance&#44; a resurgence accompanied by machine learning&#46; In the current era&#44; there is advanced AI that has the most recent developments such as deep learning&#44; neural networks&#44; and AI systems like GPT-4&#46;<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7&#8211;11</span></a></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Impact on various sectors</span><p id="par0025" class="elsevierStylePara elsevierViewall">Modern AI is transforming multiple industries&#44; redefining processes&#44; and creating new possibilities&#46; AI is having a significant impact in the field of security&#44; both in terms of national security and cybersecurity&#46; This impact ranges from improved surveillance and reconnaissance capabilities to defense against cybersecurity threats&#46; AI offers improved capabilities to protect nations and individuals from a variety of threats&#46; The use of AI in this field must be carefully managed to ensure respect for ethics and human rights and to avoid creating new vulnerabilities&#46; Moreover&#44; AI is redefining the financial sector by offering opportunities to improve efficiency&#44; decision making&#44; and the personalization of services&#46; However&#44; it also brings with it complexities in terms of risk management and data privacy&#46; To fully capitalize on the benefits of AI in finance&#44; it is crucial to address these challenges through appropriate regulations&#44; transparency&#44; and a thorough understanding of the technology&#8217;s capabilities and limitations&#46; Collaboration among financial institutions&#44; regulators&#44; and technology experts will be key to forging a financial future that is innovative&#44; secure&#44; and ethical&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">In addition&#44; in terms of predictive analyses&#44; AI is able to predict potential risks by analyzing market trends and past behavior&#46; This is particularly useful in the early identification of credit&#44; market&#44; and operational risks&#46; Likewise&#44; AI algorithms can detect suspicious activity that could indicate fraud&#44; money laundering&#44; or breach of financial regulations&#44; thus improving security and regulatory compliance&#46; Financial technology &#40;FinTech&#41; startups are using AI to innovate in areas such as payments&#44; lending&#44; and insurance&#44; challenging traditional financial institutions&#46; Finally&#44; AI&#8217;s use of personal and financial data raises important concerns in terms of data privacy and security&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#8211;20</span></a></p></span></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Basic ethical principals</span><p id="par0035" class="elsevierStylePara elsevierViewall">To what degree are people dependent on virtual assistants or recommendation systems&#63; It is crucial to respect patients&#8217; ability and right to make voluntary&#44; informed decisions about their medical care&#46; The impact on human decision making must be analyzed by examining how AI influences or dictates human decisions in different contexts&#44; from daily life to critical situations&#46; Thus&#44; how might this limit or change the autonomous decision-making process&#63;</p><p id="par0040" class="elsevierStylePara elsevierViewall">The autonomy of machines must also be analyzed by examining the degree to which machines that use AI have &#8220;autonomy&#8221; and the ethical challenges this poses&#46; To what extent should machines make independent decisions&#44; especially in critical contexts such as autonomous driving or medical care&#63;</p><p id="par0045" class="elsevierStylePara elsevierViewall">The existence of beneficence is also very important&#44; because it constitutes the commitment to act in the patient&#8217;s best interest&#44; maximizing benefits and minimizing harm&#46; As well as the duty not to cause harm&#44; which is essential in the Hippocratic oath of &#8220;first&#44; do no harm&#46;&#8221; We must also respect the equity in the distribution of medical resources and the treatment of patients&#44; ensuring that everyone is treated fairly and equitably&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">The challenges in assigning liability among developers&#44; users&#44; and AI itself must be discussed&#46; This analysis could include discussing existing laws and regulations and how they may need to be adapted to address these issues&#46; It is mandatory to address the issue of who is liable when an AI makes an error or causes harm&#44; exploring scenarios such as autonomous vehicle accidents or medical diagnosis errors made by AI&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">Finally&#44; it is necessary to reflect on how AI affects individual privacy&#44; especially in the collection and analysis of large amounts of personal data&#46; Examples include surveillance using AI and the use of personal data for targeted advertising&#46; In addition&#44; the challenges and effects of AI on privacy at a group or societal level&#44; such as the potential for extensive social profiling and the monitoring of groups or communities&#44; should be considered&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">&#8220;Explainability&#8221;</span><p id="par0060" class="elsevierStylePara elsevierViewall">The concept of &#8220;explainability&#8221; is crucial it is gaining significant attention in both AI development and in ethical discussions&#46; Explainability refers to the ability of an AI system to explain its decisions&#44; processes&#44; and actions in a way that is understandable to humans&#46; It bridges the gap between advanced technological capabilities and human-centered operations&#44; ensuring that AI systems are not only powerful and efficient&#44; but are also in line with ethical standards&#44; regulatory requirements&#44; and societal expectations&#46; As AI continues to evolve&#44; explainability will play a crucial role in fostering trust&#44; responsibility&#44; and transparency in AI systems&#46;<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">21&#8211;30</span></a></p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Trust and transparency</span><p id="par0065" class="elsevierStylePara elsevierViewall">For users to trust and interact effectively with AI systems&#44; they need to understand how these systems make decisions&#46; This is especially vital in high-risk areas such as healthcare&#44; finance&#44; and legal systems&#46; Explainability ensures transparency in AI operations&#44; allowing users to see what data the AI system uses and how it processes those data to make a decision&#46; When AI systems can explain their decisions&#44; it becomes easier to determine the liability for these decisions&#44; which is particularly important when a decision leads to negative consequences&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">On the other hand&#44; explainable AI allows for better ethical oversight&#44; as the reasons behind decisions can be examined in terms of equity&#44; bias&#44; and alignment with societal values&#46; Users can interact more effectively with AI systems if they understand the rationale behind the results and this understanding can lead to better integration of AI into everyday tasks&#46; In addition&#44; explainability allows users to provide more accurate feedback on AI decisions&#44; facilitating the continuous improvement of the AI system&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">Regulations around AI increasingly require transparency and liability&#46; Explainability can help in compliance with these legal requirements and standards&#46; In addition&#44; as international standards for AI are developed&#44; explainability will likely become a key component&#44; ensuring that AI systems can be used and trusted globally&#46;<a class="elsevierStyleCrossRefs" href="#bib0155"><span class="elsevierStyleSup">31&#8211;35</span></a></p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Facilitation of reliable AI</span><p id="par0080" class="elsevierStylePara elsevierViewall">Explainability is a cornerstone of ethical AI development&#44; as it ensures that AI systems are in line with human values and ethical principles&#46; Likewise&#44; it is necessary to move away from opaque AI systems to others in which the decision-making process is clear and understandable&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">Some AI models&#44; particularly deep learning systems&#44; are inherently complex and not easily interpretable&#46; Sometimes&#44; the most accurate AI models are the least explainable and vice versa&#46; There must be a balance between performance and explainability&#46; In regard to the development of universal standards&#44; there is no single approach to explainability&#59; what&#8217;s more&#44; what is sufficient in one context may not be sufficient in another&#46;<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">36&#8211;38</span></a></p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Different ethical theories in regard to artificial intelligence</span><p id="par0090" class="elsevierStylePara elsevierViewall">This ethical theory focuses on maximizing happiness or general welfare&#46; In the context of AI&#44; a utilitarian approach would evaluate AI actions and decisions according to their ability to maximize collective welfare&#46; As for its application in AI&#44; AI systems could be designed with the aim of maximizing general welfare&#44; but the challenge of defining and measuring said &#8220;welfare&#8221; and balancing the interests of different groups then arise&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">On the other hand&#44; the theory of deontology is based on compliance with ethical standards and duties&#46; From this perspective&#44; actions are ethically correct if they adhere to a set of established rules&#46; Its application in AI would involve programming AI to follow predefined ethical rules&#46; However&#44; the rigidity of deontology may not be suitable for all the complex situations that AI could face&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">The virtue ethics focuses on the moral characteristics and virtues of the moral agent&#44; rather than on rules or consequences&#46; As for its application in AI&#44; it would seek to translate into developing AI that exhibits or promotes qualities such as empathy or justice&#46; However&#44; programming these &#8220;virtues&#8221; into an AI is a significant challenge&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">Ethics of care also exist&#46; This emphasizes the importance of relationships and mutual care&#44; bringing interconnectedness and dependence to the foreground&#46; As for its application in AI&#44; you could seek to develop AI systems that prioritize and foster caring and supportive relationships&#44; especially in areas such as health or education&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">On the other hand&#44; pragmatism focuses on the practical consequences and adaptability of ethical standards in different contexts&#46; In regard to its application in AI&#44; this theory would suggest developing AI that is capable of ethically adapting to different situations&#44; which poses challenges in terms of programming and autonomous decision making&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">The theory of justice is based on the principles of equity&#44; justice&#44; and rights&#46; It emphasizes equality and the equitable distribution of resources and opportunities&#46; In terms of its application in AI&#44; it would involve ensuring that AI systems operate in a fair and equitable manner without discriminatory bias and that their development and use do not increase inequality&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">Finally&#44; the relational ethics considers that ethical decisions must be based on the context of relationships and community&#46; Its application in AI would focus on how AI affects human relationships and social dynamics&#44; seeking to minimize negative impacts and promote collective benefits&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">Each of these theories offers unique perspectives on how ethics should be addressed in the development and application of AI&#46; The choice of one theory over another may depend on the specific context&#44; the aims of the AI system&#44; and the priorities of developers and users&#46; Given that AI affects a wide range of areas&#44; a hybrid&#44; flexible ethical approach that can adapt to different situations and challenges is likely needed&#46;<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">39&#8211;44</span></a></p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Future of ethics in AI</span><p id="par0130" class="elsevierStylePara elsevierViewall">The possibility that future developments in AI will lead to systems that can display forms of consciousness or advanced autonomy has profound ethical and philosophical implications&#46; Reflecting on these possibilities involves exploring fundamental questions about the nature of consciousness&#44; identity&#44; and ethics in relation to non-human entities&#46; Some of these issues are analyzed below&#46;</p><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">The nature of consciousness in AI</span><p id="par0135" class="elsevierStylePara elsevierViewall">One of the fundamental challenges is to define what exactly &#8220;consciousness&#8221; means in the context of AI&#46; Can AI truly be conscious in the same sense as humans are or would its &#8220;consciousness&#8221; be something fundamentally different&#63; If it is accepted that AI can be conscious&#44; this leads to ethical questions about its rights&#44; its treatment&#44; and respect for its subjective &#8220;experience&#46;&#8221;</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Advanced autonomy</span><p id="par0140" class="elsevierStylePara elsevierViewall">Advanced autonomy in AI refers to the ability to make complex decisions independently&#46; This raises questions about liability and accountability&#44; especially in critical or high-risk situations&#46; As AI becomes more autonomous&#44; the issue arises of how to maintain control over these technologies and ensure that their decisions are aligned with human values and norms&#46;<a class="elsevierStyleCrossRefs" href="#bib0225"><span class="elsevierStyleSup">45&#8211;49</span></a></p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Ethical implications</span><p id="par0145" class="elsevierStylePara elsevierViewall">If AI is considered conscious or highly autonomous&#44; should it have rights&#63; How would these rights be defined and protected&#63; The emergence of conscious or autonomous AI could fundamentally transform the way humans interact with machines&#44; defying our current conceptions of the relationship between humans and technology&#46;</p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Rights of AI</span><p id="par0150" class="elsevierStylePara elsevierViewall">As AI systems become more advanced and autonomous&#44; the question of whether they should be considered entities with rights arises&#46; This includes questions about whether AI can or should have any kind of legal status or &#8220;personhood&#46;&#8221; If AI systems are granted rights&#44; questions about liability also arise&#46; How do you hold an AI system accountable for its actions and decisions&#63; In addition&#44; how do you balance its autonomy with public safety and welfare&#63;</p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Advanced AI security</span><p id="par0155" class="elsevierStylePara elsevierViewall">As AI becomes more autonomous and capable&#44; the potential for misuse or malfunction also increases&#46; This could have serious consequences for public and global security&#46; Establishing frameworks to control and regulate AI is essential in order to prevent abuse and ensure that the technology is used safely and ethically&#46; This includes safeguards against autonomous decision making in critical areas such as defense and national security&#46;</p></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">AI in global decision making</span><p id="par0160" class="elsevierStylePara elsevierViewall">AI has the potential to significantly influence global politics and the economy through its ability to do tasks that range from automating data analysis to predicting economic and political trends&#46; In addition&#44; AI can play a role in decision making in areas such as climate change&#44; resource management&#44; and diplomacy&#46; This raises ethical questions about representativeness and the inclusion of diverse human perspectives in such decisions&#46;<a class="elsevierStyleCrossRefs" href="#bib0250"><span class="elsevierStyleSup">50&#8211;53</span></a></p></span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Regulation and legislation</span><p id="par0165" class="elsevierStylePara elsevierViewall">A legal framework must be developed that specifically addresses the unique challenges posed by AI&#44; including issues of liability&#44; intellectual property rights&#44; and data use&#46; It is also necessary to foster the creation of international regulations on AI&#44; especially in areas in which the technology transcends national borders&#44; such as cybersecurity and data privacy&#46;<a class="elsevierStyleCrossRefs" href="#bib0270"><span class="elsevierStyleSup">54&#44;55</span></a></p></span></span><span id="sec0095" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Medical ethics and artificial intelligence</span><p id="par0170" class="elsevierStylePara elsevierViewall">AI has enormous potential in medicine&#44; but it also faces great challenges&#46; There is a wide range of applications for AI in medicine that will require a variety of authors&#44; editors&#44; and reviewers with knowledge of AI&#46; Experience in AI is linked to commercial applications and thus&#44; transparency about potential conflicts of interest will be necessary&#46; Furthermore&#44; in medicine&#44; AI must be subjected to rigorous studies&#44; like in other fields&#44; though there are challenges such as misalignment between the development and application data&#46;<a class="elsevierStyleCrossRefs" href="#bib0280"><span class="elsevierStyleSup">56&#44;57</span></a></p><p id="par0175" class="elsevierStylePara elsevierViewall">As AI becomes a more common tool in healthcare&#44; its ethical implications will become increasingly critical&#46; Advances such as those described in Moore&#8217;s law allowed for an exponential increase in storage capacity and computational power&#46; This made the development of machine learning possible&#44; with applications such as reading medical images and early detection of disease outbreaks&#46; Even so&#44; there are still challenges to address&#44; such as the standardization of clinical research with AI&#44; the control of bias in data&#44; privacy protection&#44; and the definition of ethical standards&#46; Chatbots can be useful as an aid in documentation&#44; but it is necessary to validate the accuracy of their responses&#46; AI will improve medical professionals&#8217; work by freeing up time for patient care&#44; but it will require joint adaptation between professionals and these tools&#46;<a class="elsevierStyleCrossRefs" href="#bib0290"><span class="elsevierStyleSup">58&#44;59</span></a></p><p id="par0180" class="elsevierStylePara elsevierViewall">AI has enabled advances in infectious disease surveillance through early warning systems&#44; pathogen classification&#44; risk assessment&#44; identification of focal points of contagion&#44; and epidemiological surveillance&#46; It has allowed for the creation of HealthMap for the early detection of outbreaks&#44; AI models to automate microbiological readings&#44; and the detection of antimicrobial resistance&#46; There are also reinforcement learning applications that optimally assign COVID-19 tests and the EDS-HAT system to track hospital outbreaks&#46; However&#44; challenges include data quality and representativeness&#44; privacy concerns&#44; and the inability of AI to replace international cooperation&#46; AI improves surveillance&#44; but requires a rigorous methodology that controls for bias and critically evaluates the generalization of the models&#46; Its successful implementation depends on global cooperation&#46;<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">60</span></a> Lastly&#44; AI is revolutionizing various aspects of the health field&#44; including medical diagnostics&#44; treatment personalization&#44; and pharmaceutical research&#46; This work will now explore how AI is impacting these three areas&#46;</p><span id="sec0100" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Medical diagnostics</span><p id="par0185" class="elsevierStylePara elsevierViewall">AI is being used to detect diseases in early stages&#44; significantly improving the chances of successful treatment&#46; For example&#44; AI algorithms in radiology can identify subtle signs of diseases such as cancer that might go unnoticed by the human eye&#46;<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">61</span></a> In addition&#44; AI is also revolutionizing the analysis of medical images such as X-rays&#44; magnetic resonance imaging &#40;MRI&#41; tests&#44; and computed tomography &#40;CT&#41; scans&#44; providing faster and more accurate diagnoses&#46; <a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">62</span></a></p></span><span id="sec0105" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Treatment personalization</span><p id="par0190" class="elsevierStylePara elsevierViewall">AI allows for more personalized medicine&#44; tailoring treatments to each patient&#8217;s individual characteristics&#46; This includes considering the patient&#8217;s genetics&#44; environment&#44; and lifestyle to formulate more effective treatments&#46; Likewise&#44; AI systems can help monitor and manage chronic diseases&#44; adjusting treatments in real time based on the patient&#8217;s response&#46;</p></span><span id="sec0110" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Pharmaceutical research</span><p id="par0195" class="elsevierStylePara elsevierViewall">AI is accelerating the discovery and development of new drugs&#44; rapidly analyzing vast amounts of data to identify potential compounds and predict their efficacy and safety&#46;<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">61</span></a> It can also help in optimizing clinical trials in aspects that range from candidate selection to monitoring outcomes and side effects&#46;<a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">63</span></a></p></span><span id="sec0115" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Specific examples</span><p id="par0200" class="elsevierStylePara elsevierViewall">Some AI algorithms are used to detect breast&#44; skin&#44; and other cancers based on medical images&#44; often with greater accuracy than traditional methods&#46; In addition&#44; smartphone applications that use AI to monitor vital signs and symptoms provide personalized analysis and recommendations&#46;</p></span><span id="sec0120" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Autonomy and informed consent</span><p id="par0205" class="elsevierStylePara elsevierViewall">A major challenge is ensuring that patients understand how AI will influence their diagnosis and treatment&#46; This is crucial for genuine informed consent&#46; AI should be used as a tool to supporting&#8212;not replacing&#8212;shared decision making between the physician and the patient&#44; respecting patient autonomy&#46;</p></span><span id="sec0125" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0145">Privacy and data confidentiality</span><p id="par0210" class="elsevierStylePara elsevierViewall">Health data are extremely sensitive&#46; AI in medicine requires robust safeguards to protect these data against unauthorized access and security breaches&#46; Patients must have control over how their data are used and it is essential to obtain consent for the use of their data in the training and application of AI models&#46;</p></span><span id="sec0130" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0150">Quality and equity in care</span><p id="par0215" class="elsevierStylePara elsevierViewall">AI algorithms can perpetuate inherent biases in the training data&#46; It is crucial to develop strategies to identify and mitigate these biases to avoid disparities in healthcare&#46; In addition&#44; it must be guaranteed that advances in AI do not create a divide in medical care in which only certain groups have access to the best possible care&#46;</p></span><span id="sec0135" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0155">Liability and decision making</span><p id="par0220" class="elsevierStylePara elsevierViewall">In cases of diagnostic or treatment errors involving AI&#44; it is essential to determine who is liable&#46; Is it the AI developer&#44; the medical professional who uses it&#44; or both&#63;</p><p id="par0225" class="elsevierStylePara elsevierViewall">AI systems must be transparent and their decision-making processes must be explainable so that medical professionals can trust and understand their recommendations&#46;</p></span><span id="sec0140" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0160">Challenges in research and development</span><p id="par0230" class="elsevierStylePara elsevierViewall">All AI development and research in medicine must be done in an ethical manner&#44; respecting the rights of participants and prioritizing their welfare&#46; Collaboration among experts in technology&#44; ethics&#44; law&#44; and medicine is vital&#46; What&#8217;s more&#44; it is necessary to train health professionals on the ethical and practical aspects of AI&#46;</p><p id="par0235" class="elsevierStylePara elsevierViewall">The accuracy of AI when preparing for internal medicine exams&#44; such as the boards in the United States of America&#44; is notable due to several factors&#46;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">&#8226;</span><p id="par0240" class="elsevierStylePara elsevierViewall">Analysis of data and patterns&#58; AI can process and analyze large volumes of medical data&#44; identifying patterns and trends that may be difficult for humans to discern&#46; This is particularly useful in test preparation&#44; in which understanding patterns in diagnoses and treatment can be key&#46;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">&#8226;</span><p id="par0245" class="elsevierStylePara elsevierViewall">Personalization of learning&#58; AI can adapt the study materials to students&#8217; individual needs&#46; By analyzing previous responses and progress in studying&#44; AI can identify weak areas and strengthen them with additional resources&#46;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">&#8226;</span><p id="par0250" class="elsevierStylePara elsevierViewall">Continuous content updating&#58; Given that the field of medicine is constantly evolving&#44; AI can keep up with the latest research and clinical guidelines&#44; ensuring that students study the most up-to-date&#44; relevant information&#46;</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">&#8226;</span><p id="par0255" class="elsevierStylePara elsevierViewall">Mock exams and assessments&#58; AI can generate practice exams that mimic the format and style of real boards&#46; This not only helps students become familiar with the exam structure&#44; but also allows them to assess their preparation effectively&#46;</p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">&#8226;</span><p id="par0260" class="elsevierStylePara elsevierViewall">Instant feedback and performance analysis&#58; AI systems can provide immediate feedback on practice questions&#44; allowing students to understand and correct errors instantly&#46; In addition&#44; they can analyze general performance to offer specific study recommendations&#46;</p></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">&#8226;</span><p id="par0265" class="elsevierStylePara elsevierViewall">Integration of multiple sources of knowledge&#58; This leads to a more holistic&#44; comprehensive approach to studying&#46;</p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">&#8226;</span><p id="par0270" class="elsevierStylePara elsevierViewall">Reduction in bias and human error&#58; by relying on objective data and algorithms&#44; AI can help reduce the personal biases and errors that often occur in traditional studying&#46;</p></li></ul></p><p id="par0275" class="elsevierStylePara elsevierViewall">However&#44; it is crucial to remember that AI is a tool for support and does not substitute human clinical judgment or the practical experience needed in internal medicine&#46; Its most effective use is in addition to traditional study techniques and clinical training&#46;<a class="elsevierStyleCrossRefs" href="#bib0320"><span class="elsevierStyleSup">64&#8211;68</span></a></p><p id="par0280" class="elsevierStylePara elsevierViewall">There are different AI Chatbots from different companies&#44; such as Google&#39;s Bard&#44; Anthropic&#39;s Claude&#44; Microsoft Copilot&#44; and most recently&#44; Google&#8217;s Gemini&#46; Each has its own characteristics&#46; In the case of ChatGPT&#44; developed by OpenAI&#44; the chronology of the emergence and updating of its different versions is as follows&#58;<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel">&#8226;</span><p id="par0285" class="elsevierStylePara elsevierViewall">GPT-3&#58; Introduced in June 2020&#44; GPT-3 represented a significant leap in terms of size and complexity&#44; with 175 billion documents&#46; Its ability to generate coherent text and its versatility on a variety of tasks made it a benchmark in natural language AI&#46;</p></li><li class="elsevierStyleListItem" id="lsti0045"><span class="elsevierStyleLabel">&#8226;</span><p id="par0290" class="elsevierStylePara elsevierViewall">ChatGPT and GPT-3&#46;5&#58; ChatGPT&#44; based on a modified version of GPT-3&#46;5&#44; was launched at the end of 2022&#46; It incorporated a reinforced learning capability with human feedback&#44; improving the relevance and accuracy of responses&#46;</p></li><li class="elsevierStyleListItem" id="lsti0050"><span class="elsevierStyleLabel">&#8226;</span><p id="par0295" class="elsevierStylePara elsevierViewall">GPT-4&#58; This is the latest version&#44; updated in April 2023&#46; It features significant improvements in comprehension&#44; text generation&#44; and multitasking capabilities compared to its predecessors&#46; It is probably the most powerful chatbot at present&#46; In February 2024&#44; another large amount of data will be dumped&#46;</p></li></ul></p><p id="par0300" class="elsevierStylePara elsevierViewall">Historically&#44; general-purpose technologies have been slow to deliver on their promises&#44; a phenomenon known as the &#8220;information technology productivity paradox&#46;&#8221; The successful implementation of new technologies in healthcare is particularly challenging&#46; However&#44; generative AI has unique properties that could shorten the usual delay between implementation and gains in productivity and&#47;or quality&#46; The healthcare ecosystem has evolved to be more receptive to generative AI&#46; What&#39;s more&#44; many healthcare organizations are ready to implement the necessary innovations in culture&#44; leadership&#44; workforce&#44; and workflow&#46;</p><p id="par0305" class="elsevierStylePara elsevierViewall">The ability of generative AI to rapidly improve and the ability of organizations to implement additional innovations indicate that generative AI could deliver significant improvements in healthcare faster than previous technologies have&#46; Likewise&#44; previous healthcare technologies&#44; such as electronic medical records&#44; have followed a pattern similar to the productivity paradox&#44; offering mixed results in terms of efficiency&#46; Finally&#44; although the implementation of generative AI faces unique challenges in healthcare&#44; its distinctive characteristics and the recent evolution of the healthcare ecosystem offer a promising path forward for overcoming these challenges and achieving significant improvements&#46;<a class="elsevierStyleCrossRefs" href="#bib0345"><span class="elsevierStyleSup">69&#44;70</span></a></p></span></span><span id="sec0145" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0165">Funding</span><p id="par0310" class="elsevierStylePara elsevierViewall">No funding was received for this article&#46;</p></span><span id="sec0150" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0170">Conflicts of interest</span><p id="par0315" class="elsevierStylePara elsevierViewall">There are no conflicts of interest&#46;</p></span></span>"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context&#46; Ethics in medical artificial intelligence &#40;AI&#41; involves ensuring that technologies are safe&#44; fair&#44; and respect patient privacy&#46; This includes concerns about the accuracy of diagnoses provided by artificial intelligence&#44; fairness in patient treatment&#44; and protection of personal health data&#46;</p><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Advances in artificial intelligence can significantly improve healthcare&#44; from more accurate diagnoses to personalized treatments&#46; However&#44; it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration&#44; involving healthcare professionals&#44; artificial intelligence experts&#44; patients&#44; and ethics specialists to guide and oversee their implementation&#46;</p><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Finally&#44; transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental&#46;</p></span>"
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