ReviewBMC medical ethics2025
High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare.
Review in BMC medical ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.Healthcare (Basel, Switzerland) · 2026Review
- Legal and ethical responsibility for AI-assisted diagnostic and referral errors: a scoping review of comparative approaches with implications for Saudi healthcare.BMC medical ethics · 2026Article
- Unlocking the Full Potential of Health Care Teams: How Artificial Intelligence Can Help.JMIR AI · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- How do Engineers Experience Ethics in Practice?: A Phenomenographic Investigation of Engineers in the Health Products Industry.Science and engineering ethics · 2026Article
- Mapping the landscape of AI in healthcare in Kazakhstan: a scoping review of readiness, development, and adoption.BMC health services research · 2026Article
- Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.Scientific reports · 2026Article
- Clinical Use of Non-Certified Generative AI in Healthcare: Governing the Regulatory Grey Zone from Convenience to Legal Accountability.Journal of medical systems · 2026Article
- Current Applications and Future Perspectives of Artificial Intelligence in Face-Driven Orthodontics: A Scoping Review.Biomimetics (Basel, Switzerland) · 2026Review
- Development of an evaluation indicator system for the quality of science and technology ethics review committees: a Chinese study based on the structure-process-outcome framework.BMC medical ethics · 2026Article
- Beyond the hype: navigating the real-world applications of artificial intelligence (AI) in healthcare.Medical review (2021) · 2026Review
- Research trends and ethical perspectives on explainable artificial intelligence in emergency medicine: a bibliometric analysis.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Review
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Artificial intelligence in anesthesiology education: transformative applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
- Twelve Practical Tips for Integrating AI Into Medical Education: Tutorial to Support Educators Across Teaching, Research, Administration, and Ethical Domains.JMIR medical education · 2025Article
- On the translational potential of atlases in precision oncology.Translational cancer research · 2025Review
- Postgraduate nursing students' knowledge, attitudes, and practices regarding artificial intelligence: a qualitative study.BMC medical education · 2025Article
- Extraneural Soft Tissue Perineurioma of the Oral Cavity: A Rare Case with Medico-Legal Implications and Literature Review.Life (Basel, Switzerland) · 2025Review
- Integrating Precision Medicine and Digital Health in Personalized Weight Management: The Central Role of Nutrition.Nutrients · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConsidering the disruptive potential of AI technology, its current and future impact in healthcare, as well as healthcare professionals' lack of training in how to use it, the paper summarizes how to approach the challenges of AI from an ethical and legal perspective. It concludes with suggestions for improvements to help healthcare professionals better navigate the AI wave.
methodsWe analyzed the literature that specifically discusses ethics and law related to the development and implementation of AI in healthcare as well as relevant normative documents that pertain to both ethical and legal issues. After such analysis, we created categories regrouping the most frequently cited and discussed ethical and legal issues. We then proposed a breakdown within such categories that emphasizes the different - yet often interconnecting - ways in which ethics and law are approached for each category of issues. Finally, we identified several key ideas for healthcare professionals and organizations to better integrate ethics and law into their practices.
resultsWe identified six categories of issues related to AI development and implementation in healthcare: (1) privacy; (2) individual autonomy; (3) bias; (4) responsibility and liability; (5) evaluation and oversight; and (6) work, professions and the job market. While each one raises different questions depending on perspective, we propose three main legal and ethical priorities: education and training of healthcare professionals, offering support and guidance throughout the use of AI systems, and integrating the necessary ethical and legal reflection at the heart of the AI tools themselves.
conclusionsBy highlighting the main ethical and legal issues involved in the development and implementation of AI technologies in healthcare, we illustrate their profound effects on professionals as well as their relationship with patients and other organizations in the healthcare sector. We must be able to identify AI technologies in medical practices and distinguish them by their nature so we can better react and respond to them. Healthcare professionals need to work closely with ethicists and lawyers involved in the healthcare system, or the development of reliable and trusted AI will be jeopardized.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.