Artificial Intelligence in Medicine: Weighing the Accomplishments, Hype, and Promise

The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as "artificial intelligence," "deep learning," "machine learning," "forensic medicine," "legal medicine," "forensic pathology" and "medicine" were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

The coming of age of artificial intelligence in medicine.

The rapid adoption and implementation of artificial intelligence in medicine creates an ontologically distinct situation from prior care models. There are both potential advantages and disadvantages with such technology in advancing the interests of patients, with resultant ontological and epistemic concerns for physicians and patients relating to the instatiation of AI as a dependent, semi- or fully-autonomous agent in the encounter. The concept of libertarian paternalism potentially exercised by AI (and those who control it) has created challenges to conventional assessments of patient and physician autonomy. The unclear legal relationship between AI and its users cannot be settled presently, an progress in AI and its implementation in patient care will necessitate an iterative discourse to preserve humanitarian concerns in future models of care. This paper proposes that physicians should neither uncritically accept nor unreasonably resist developments in AI but must actively engage and contribute to the discourse, since AI will affect their roles and the nature of their work. One’s moral imaginative capacity must be engaged in the questions of beneficence, autonomy, and justice of AI and whether its integration in healthcare has the potential to augment or interfere with the ends of medical practice.

Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

The article researches the problem of ethical support of the application of artificial intelligence (AI) in medicine and healthcare, which is topical for modern sci­ence. Despite a significant number of foreign and domestic publications devoted to the topic of AI, the conceptual justification of the ethics of AI application in medicine and healthcare remains poorly developed. Relying on international recommendations and articles, as well as on their own experience of research activities, work in research ethics committees, the results of a pilot survey of health care workers, etc., the authors define and analyze the basic ethical principles of using AI in medicine and health care. The proposed principles are considered in the context of their practical application to protect human and natural rights and interests, which includes preservation of patient confidentiality, prevention of discrimination, protection from AI errors, respect for in­formed consent, as well as compliance with the norms of “open science”, mutual trust of developers and users, etc. The proposed principles are analyzed in the context of their practical application. The application of the proposed principles will orient scientists, AI developers, ethical committees conducting expert review of research, society as a whole to the priorities of humanization of healthcare, respect for human beings and nature, as well as to educate society, create a regulatory framework, ethical recommen­dations and codes of ethics for the use of AI in medicine and healthcare.

In the era of advancements in artificial intelligence (AI) and machine learning, the healthcare industry has become one of the major areas where such technologies are being actively adopted and utilized. The global health care sector generated more than 2.3 zettabytes of data worldwide in 2020. Analysts estimate that the global market for artificial intelligence (AI) in medicine will grow to $13 billion by 2025, with a significant increase in newly established companies. Artificial intelligence in medicine is used to predict, detect and diagnose various diseases and pathologies. The sources of data can be various results of medical research (EEG, X-ray images, laboratory tests, e.g. tissues, etc.). At the same time, there are understandable concerns that AI will undermine the patient-provider relationship, contribute to the deskilling of providers, undermine transparency, misdiagnose or inappropriately treat because of errors within AI decision-making that are hard to detect, exacerbate existing racial or societal biases, or introduce algorithmic bias that will be hard to detect. Traditional research methods, general and special ones, with an emphasis on the comparative legal method, were chosen. For the AI to work it needs to be trained, and it’s learning from all sorts of information given to it. The main part of the information on which AI is trained is health data, which is sensitive personal data. The fact that personal data is qualified as sensitive personal data indicates the significance of the information contained, the high risks in case it’s leaking, and hence the need for stricter control and regulation. The article offers a detailed exploration of the legal implications of AI in medicine, highlighting existing challenges, the current state of regulation, and proposes future perspectives and recommendations for legislation adapted to the era of medical AI. Given the above, the study is divided into three parts: international framework, that will focus primarily on applicable WHO documents; risks and possible ways to minimize them, where the authors have tried to consider various issues related to the use of AI in medicine and find options to address them; and relevant case-study.

Chapter 11 - The Future of Artificial Intelligence in Medicine

Chapter 24 - Industry perspectives and commercial opportunities of artificial intelligence in medicine

The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions.

Background: Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of industrialization of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the “2nd Meeting of Science and Dialysis: Artificial Intelligence,” organized in the Bellvitge University Hospital, Barcelona, Spain. Summary and Key Messages: AI and ML are attracting much interest from the medical community as key approaches to knowledge extraction from data. These approaches are increasingly colonizing ambits of social impact, such as medicine and healthcare. Issues of social relevance with an impact on medicine and healthcare include (although they are not limited to) fairness, explainability, privacy, ethics and legislation.

The rate of development in artificial intelligence (AI) technologies has brought revolutionary transformationsacross the healthcare sector. The advancements especially impacted the manner in which the diagnosis,treatment, and management of diseases are undertaken with a growing emphasis laid on optimizing clinicalworkflow and patient care. Machine learning (ML) and deep learning (DL) technologies have proved invaluablefor the analysis of complex datasets, supporting early diagnosis and optimizing treatment protocols. AIshowcased tremendous promise in a plethora of applications, such as the accurate analysis of medical imagery,the personalization of therapies through the tailor-made dosing of medicines, predictive analysis in forecastingthe course of the disease, and the assistance of patients and clinicians by deploying virtual health tools.Moreover, the increasing application of AI in healthcare has also boosted access to quality healthcare,particularly in underserved populations. The following review also explores the ethical, legislative, andregulatory issues related to the application of AI in medicine, reiterating the need for protection of the data,transparency, and equitable access. With careful implementation that respects these boundaries, AI holds thepotential to significantly enhance global healthcare delivery. Despite these benefits, integrating AI intohealthcare systems raises important ethical, legal, and regulatory concerns. Ensuring data privacy, promotingalgorithm transparency, and maintaining fairness in AI-driven healthcare solutions are key concerns.Addressing these challenges is vital to building trust and ensuring that AI technologies benefit all populationsfairly. With responsible and ethical implementation, AI holds immense potential to transform global healthcaredelivery and improve patient outcomes on a large scale. How to cite this: Jan TA, Naz S. Artificial Intelligence in Medicine: Transforming The Future of Healthcare. Life and Science. 2025; 6(2): 284-291. doi: http://doi.org/10.37185/LnS.1.1.904

The role of artificial intelligence in medicine is rapidly expanding. From machine learning diagnostic algorithms to neural networks for electronic health record mining, the diverse applications for artificial intelligence in medicine seem to be endless. The vast amount of physiological and electronic health data generated in the perioperative period has created an opportunity for artificial intelligence to play important roles in anesthesiology, including preoperative risk stratification, intelligent patient monitoring, clinical decision support and personalized perioperative guidelines. In this chapter, we review how data science and artificial intelligence is currently playing a role in personalized anesthesiology and perioperative medicine, as well as lay out a vision for the future directions of this growing field.

Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

To assess the knowledge and perception of medical students regarding the utility and applications of artificial intelligence in medicine. The cross-sectional study was conducted at the Shifa College of Medicine, Islamabad, Pakistan, from February to August 2021, and comprised medical students regardless of gender or year of studies. Data was collected using a pretested questionnaire. Differences in perceptions were explored relative to gender and the year of studies. Data was analysed using SPSS 23. Of the 390 participants, 168(43.1%) were males and 222(56.9%) were females. The overall mean age was 20±1.65 years. There were 121(31%) students from the first year of studies, 122(31.3%) second year, 30(7.7%) from third year, 73(18.7%) from fourth year, and 44(11.3%) from the fifth year. Most participants 221(56.7%) had a good familiarity with artificial intelligence, and 226(57.9%) agreed that the biggest advantage of using artificial intelligence in healthcare was its ability to speed up the processes. In terms of gender of year of studies, there were no significant differences on both counts (p>0.05). Medical students, regardless of age and year of studies, were found to have a good understanding of the usage and application of artificial intelligence in medicine.

In this intervention, I examine artificial intelligence (AI) in agriculture through a political ecology lens, analysing how promises of productivity, efficiency, and sustainability take shape across uneven postcolonial landscapes. Building on feminist and critical agrarian perspectives, I focus on the material relations of farming to show that AI in agriculture, often portrayed as immaterial, relies on deeply material infrastructures—sensors, data centres, energy systems, and extractive supply chains. Tracing how AI‐driven digital agriculture and smart agriculture reconfigure relations between farmers, land, and technology, I argue that care for agroecosystems is increasingly displaced by care for digital infrastructures, with dire consequences for the social and ecological relations that sustain rural labour, land, and livelihoods. Focusing on emergent applications of AI in agriculture in the Global South, I show how these technologies consolidate corporate power, erode agrarian knowledges, and reproduce postcolonial inequalities under neoliberal capitalism. I conceptualise AI as a socioecological fix, stressing the need to re‐spatialise and historicise technological change in the context of agrarian change, by attending to its infrastructural and ecological entanglements. I situate this analysis within debates on economies of repair, arguing that understanding AI in agriculture requires attention not only to breakdown and failure but also to the everyday practices of repair—technical, ecological, and social—that sustain digital infrastructures and reveal the uneven burdens of maintaining them across agrarian worlds. This intervention contributes to the Themed Intervention, 'Geographies of Responsibility, Care and Repair in Digital Worlds of AI'.