Physicians' Ethical Concerns About Artificial Intelligence in Medicine: a Qualitative Study:

Overview

Journal Front Public Health

Specialty Public Health

Date 2024 Dec 12

PMID 39664534

Authors

Fatma Kahraman

Aysenur Aktas

Serra Bayrakceken

Tuna Cakar

Hande Serim Tarcan

Bugrahan Bayram

Berk Durak

Yesim Isil Ulman

Affiliations

Soon will be listed here.

Abstract

Background/aim: Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance.

Methods: This qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare.

Results: Content analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., , , and . Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional.

Conclusion: The authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity.

References

Ahuja A . The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 2019; 7:e7702. PMC: 6779111. DOI: 10.7717/peerj.7702. View

Chen Y, Stavropoulou C, Narasinkan R, Baker A, Scarbrough H . Professionals' responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study. BMC Health Serv Res. 2021; 21(1):813. PMC: 8364018. DOI: 10.1186/s12913-021-06861-y. View

Awad E, Dsouza S, Kim R, Schulz J, Henrich J, Shariff A . The Moral Machine experiment. Nature. 2018; 563(7729):59-64. DOI: 10.1038/s41586-018-0637-6. View

Grote T, Berens P . On the ethics of algorithmic decision-making in healthcare. J Med Ethics. 2019; 46(3):205-211. PMC: 7042960. DOI: 10.1136/medethics-2019-105586. View

Lin S . A Clinician's Guide to Artificial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution. J Am Board Fam Med. 2022; 35(1):175-184. DOI: 10.3122/jabfm.2022.01.210226. View

Amann J, Blasimme A, Vayena E, Frey D, Madai V . Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020; 20(1):310. PMC: 7706019. DOI: 10.1186/s12911-020-01332-6. View

Arnold M . Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine. J Bioeth Inq. 2021; 18(1):121-139. PMC: 7790358. DOI: 10.1007/s11673-020-10080-1. View

Kiener M . Artificial intelligence in medicine and the disclosure of risks. AI Soc. 2020; 36(3):705-713. PMC: 7580986. DOI: 10.1007/s00146-020-01085-w. View

Magrabi F, Ammenwerth E, Brender McNair J, De Keizer N, Hypponen H, Nykanen P . Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearb Med Inform. 2019; 28(1):128-134. PMC: 6697499. DOI: 10.1055/s-0039-1677903. View

10.

Jackson B, Ye Y, Crawford J, Becich M, Roy S, Botkin J . The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice. Acad Pathol. 2021; 8:2374289521990784. PMC: 7894680. DOI: 10.1177/2374289521990784. View

11.

Price W . Big data and black-box medical algorithms. Sci Transl Med. 2018; 10(471). PMC: 6345162. DOI: 10.1126/scitranslmed.aao5333. View

12.

Harris J . Who Owns My Autonomous Vehicle? Ethics and Responsibility in Artificial and Human Intelligence. Camb Q Healthc Ethics. 2018; 27(4):599-609. DOI: 10.1017/S0963180118000038. View

13.

McDougall R . No we shouldn't be afraid of medical AI; it involves risks and opportunities. J Med Ethics. 2019; 45(8):559. DOI: 10.1136/medethics-2019-105572. View

14.

Liu P, Lu L, Zhang J, Huo T, Liu S, Ye Z . Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. 2021; 41(6):1105-1115. PMC: 8648557. DOI: 10.1007/s11596-021-2474-3. View

15.

Van Cauwenberge D, Van Biesen W, Decruyenaere J, Leune T, Sterckx S . "Many roads lead to Rome and the Artificial Intelligence only shows me one road": an interview study on physician attitudes regarding the implementation of computerised clinical decision support systems. BMC Med Ethics. 2022; 23(1):50. PMC: 9077861. DOI: 10.1186/s12910-022-00787-8. View

16.

Obermeyer Z, Emanuel E . Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016; 375(13):1216-9. PMC: 5070532. DOI: 10.1056/NEJMp1606181. View

17.

Lai M, Brian M, Mamzer M . Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J Transl Med. 2020; 18(1):14. PMC: 6953249. DOI: 10.1186/s12967-019-02204-y. View

18.

Davenport T, Kalakota R . The potential for artificial intelligence in healthcare. Future Healthc J. 2019; 6(2):94-98. PMC: 6616181. DOI: 10.7861/futurehosp.6-2-94. View

19.

Char D, Shah N, Magnus D . Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018; 378(11):981-983. PMC: 5962261. DOI: 10.1056/NEJMp1714229. View

20.

Starke G, Ienca M . Misplaced Trust and Distrust: How Not to Engage with Medical Artificial Intelligence. Camb Q Healthc Ethics. 2022; 33(3):360-369. DOI: 10.1017/S0963180122000445. View