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Artificial intelligence (AI) has the potential to revolutionize healthcare, but is unlikely to fully replace human doctors. This paper explores the limitations of AI in healthcare, focusing on three key areas: lack of embodiment, limited understanding of meaning in everyday language, and the inability to exercise judgment and clinical reasoning. Recognizing these limitations enables us to use AI to enhance our capabilities rather than allowing it to substitute humans. Following this philosophical examination of AI's limitations, I will argue that the question of whether AI will replace doctors is a misleading one. Instead, this framework advocates for synergistic human-AI collaboration in health-care settings. It necessitates the development of hybrid entities: a physician-AI partnership and a patient-AI interface. The overarching objective is to effectively address the core mission of medicine, which is providing optimal treatment and compassionate care for all patients. This hybrid model must proactively mitigate the risks of AI integration, such as exacerbation of existing health-care challenges and potential dehumanization of patient care. Within this framework, key objectives include: reducing medical errors, fostering humane doctor-patient relationships, mitigating the trend *Corresponding Author Alireza Monajemi Address: No 4, Institute for Humanities and Cultural Studies, Iranshenasi St., Kurdestan Highway, Tehran, Iran. Zip Code: 1437774681 PO Box : 14155 Tel: (+98) 21 88 49 02 09 Email: monajemi@ihcs.ac.ir Received: 24 Dec 2024 Accepted: 6 May 2025 Published: -- May 2025 Citation to this article: Monajemi A. Why is the idea of AI completely replacing physicians a pseudo-problem? A philosophical analysis. J Med Ethics Hist Med. 2025; 18: 1. of medicalization, and ultimately improving overall public health outcomes.

This book defends medical nihilism, which is the view that we should have little confidence in the effectiveness of medical interventions. If we consider the frequency of failed medical interventions, the extent of misleading evidence in medical research, the thin theoretical basis of many interventions, and the malleability of empirical methods in medicine, and if we employ our best inductive framework, then our confidence in the effectiveness of medical interventions ought to be low. Part I articulates theoretical and conceptual groundwork, which offers a defense of a hybrid theory of disease, which forms the basis of a novel account of effectiveness, and this is applied to pharmacological science and to issues such as medicalization. Part II critically examines details of medical research. Even the very best methods in medical research, such as randomized trials and meta-analyses, are malleable and suffer from various biases. Methods of measuring the effectiveness of medical interventions systematically overestimate benefits and underestimate harms. Part III summarizes the arguments for medical nihilism and what this position entails for medical research and practice. To evaluate medical nihilism with care, the argument is stated in formal terms. Medical nihilism suggests that medical research must be modified, that clinical practice should be less aggressive in its therapeutic approaches, and that regulatory standards should be enhanced.

Background Healthcare is permeated by phenomena of vulnerability and their ethical significance. Nonetheless, application of this concept in healthcare ethics today is largely confined to clinical research. Approaches that further elaborate the concept in order to make it suitable for healthcare as a whole thus deserve renewed attention. Methods Conceptual analysis. Results Taking up the task to make the concept of vulnerability suitable for healthcare ethics as a whole involves two challenges. Firstly, starting from the concept as it used in research ethics, a more detailed characterization and systematization of the different realms of human abilities and the various ways in which these realms contain vulnerability is to be established. Secondly, at the same time, the sought-after concept of vulnerability should avoid picturing the relation between healthcare recipient and provider as a relation between a dependent individual in need and another individual capable of providing all the help necessary. An adequate concept of vulnerability should enable one to understand when and in which respects care providers may be vulnerable as well. Philosophical accounts of vulnerability can help to meet both of these challenges. Conclusions Philosophical accounts of vulnerability can help to make the concept of vulnerability suitable for healthcare ethics as a whole. They come with a price, though. While the ethical role of vulnerability in medical ethics usually is to signify states of affairs that are to be diminished or overcome, philosophical accounts introduce forms of vulnerability that are regarded as valuable. Further analyzing and systematizing forms and degrees of vulnerability thus comprises the task to distinguish between amounts and types of vulnerability that can count as valuable, and amounts and types of vulnerability that are to be alleviated.

Background Advances in healthcare artificial intelligence (AI) are occurring rapidly and there is a growing discussion about managing its development. Many AI technologies end up owned and controlled by private entities. The nature of the implementation of AI could mean such corporations, clinics and public bodies will have a greater than typical role in obtaining, utilizing and protecting patient health information. This raises privacy issues relating to implementation and data security. Main body The first set of concerns includes access, use and control of patient data in private hands. Some recent public–private partnerships for implementing AI have resulted in poor protection of privacy. As such, there have been calls for greater systemic oversight of big data health research. Appropriate safeguards must be in place to maintain privacy and patient agency. Private custodians of data can be impacted by competing goals and should be structurally encouraged to ensure data protection and to deter alternative use thereof. Another set of concerns relates to the external risk of privacy breaches through AI-driven methods. The ability to deidentify or anonymize patient health data may be compromised or even nullified in light of new algorithms that have successfully reidentified such data. This could increase the risk to patient data under private custodianship. Conclusions We are currently in a familiar situation in which regulation and oversight risk falling behind the technologies they govern. Regulation should emphasize patient agency and consent, and should encourage increasingly sophisticated methods of data anonymization and protection.

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence (AI). To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care.

Background Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. Methods PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies. Results Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. Discussion The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Conclusions Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.

As empathy gains importance within academia, we propose this review as an attempt to bring clarity upon the diverse and widely debated definitions and conceptions of empathy within the medical field. In this paper, we first evaluate the limits of the Western mainstream medical culture and discuss the origins of phenomena such as dehumanization and detached concern as well as their impacts on patient care. We then pass on to a structured overview of the debate surrounding the notion of clinical empathy and its taxonomy in the medical setting. In particular, we present the dichotomous conception of clinical empathy that is articulated in the debate around cognitive empathy and affective empathy. We thus consider the negative impacts that this categorization brings about. Finally, we advocate for a more encompassing, holistic conception of clinical empathy; one that gives value to a genuine interest in welcoming, acknowledging and responding to the emotions of those suffering. Following this line of reasoning, we advance the notion of ‘empathic concern’, a re-conceptualization of clinical empathy that finds its source in Halpern in Med Health Care Philos (2014) 17:301–311 engaged curiosity. We ultimately advance Narrative Medicine as an approach to introduce, teach and promote such an attitude among medical trainees and practitioners.

Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. The COVID-19 pandemic, however, has significantly increased the utilisation of health-oriented chatbots, for instance, as a conversational interface to answer questions, recommend care options, check symptoms and complete tasks such as booking appointments. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence clinical practices and expert–client relationships. We suggest the need for new approaches in professional ethics as the large-scale deployment of artificial intelligence may revolutionise professional decision-making and client–expert interaction in healthcare organisations. We argue that the implementation of chatbots amplifies the project of rationality and automation in clinical practice and alters traditional decision-making practices based on epistemic probability and prudence. This article contributes to the discussion on the ethical challenges posed by chatbots from the perspective of healthcare professional ethics.

Background Artificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective? Methods Eight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed. Results Upon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs). Conclusions The ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere.