CoChat
Sign Up
Cited by 84Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

AI and machine learning ethics, law, diversity, and global impact

Open access
Drabiak, Katherine, Kyzer, Skylar, Nemov, Valerie, El Naqa, Issam·2023·The British Journal of Radiology
DOI 10.1259/bjr.20220934PMC PMC10546451
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial intelligence (AI) and its machine learning (ML) algorithms are offering new promise for personalized biomedicine and more cost-effective healthcare with impressive technical capability to mimic human cognitive capabilities. However, widespread application of this promising technology has been limited in the medical domain and expectations have been tampered by ethical challenges and concerns regarding patient privacy, legal responsibility, trustworthiness, and fairness. To balance technical innovation with ethical applications of AI/ML, developers must demonstrate the AI functions as intended and adopt strategies to minimize the risks for failure or bias. This review describes the new ethical challenges created by AI/ML for clinical care and identifies specific considerations for its practice in medicine. We provide an overview of regulatory and legal issues applicable in Europe and the United States, a description of technical aspects to consider, and present recommendations for trustworthy AI/ML that promote transparency, minimize risks of bias or error, and protect the patient well-being.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1259/bjr.20220934

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Others such as Huang et al. [9] and Drabiak et al. [10] delve into the application of real-world data and the ethical challenges of ML, but rarely address whether these models meaningfully improve or exacerbate health disparities.

    “Similarly, while others such as Huang et al. [9] and Drabiak et al. [10] delve into the application of real-world data and the ethical challenges of ML, they rarely address whether these models meaningfully improve or exacerbate health disparities.”

    From Advancing Health Equity through Multi-Level Fairness in Health Informatics (2026) · cites this paper as [10]

    The Introduction discusses ethical challenges and warns that AI/ML may exacerbate racial and gender inequity; the Recommendation Sources section also calls for inclusion of underrepresented groups. The paper does not establish whether models meaningfully improve or worsen health disparities, so that limitation is fair, but saying it rarely addresses disparities overlooks its explicit discussion of the risk. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 53

  1. Shortliffe EH, Buchanan BG. A model of inexact Reasoning in medicine. Mathematical Biosciences 1975; 23(3-4): 351-79.https://doi. org/10.1016/0025-5564(75)90047-4 · DOI · Paper page
  2. Yu VL, Fagan LM, Wraith SM, Clancey WJ, Scott AC, Hannigan J, et al. Antimicrobial selection by a computer: A blinded evaluation by infectious diseases experts. JAMA 1979; 242: 1279-82.
  3. Giger ML, Huo Z, Kupinski MA, Vyborny CJ. Computer-Aided diagnosis in mammography. Handbook of medical imaging 2000; 2: 915-1004.
  4. Fenton JJ, Taplin SH, Carney PA, Abraham L, Sickles EA, D'Orsi C, et al. Influence of computer-aided detection on performance of screening mammography. New England Journal of Medicine 2007; 356: 1399-409. https://doi.org/10.1056/NEJMoa066099 · DOI
  5. Gromet M. Comparison of computer- aided detection to double reading of screening mammograms: review of 231,221 mammograms. American Journal of Roentgenology 2008; 190: 854-9.https://doi. org/10.2214/AJR.07.2812 · DOI
  6. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380: 1347-58.https://doi.org/10.1056/ NEJMra1814259 · DOI · Paper page
  7. El Naqa I, Haider MA, Giger ML, Ten Haken RK. Artificial intelligence: Reshaping the practice of radiological sciences in the 21st century. Br J Radiol 2020; 93: : 20190855. https://doi.org/10.1259/bjr.20190855 · DOI
  8. Shouval R, Fein JA, Savani B, Mohty M, Nagler A. Machine learning and artificial intelligence in haematology. Br J Haematol 2021; 192: 239- 50.https://doi.org/10.1111/bjh.16915 · DOI
  9. Patel UK, Anwar A, Saleem S, Malik P, Rasul B, Patel K, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol 2021; 268: 1623-42.https://doi.org/10.1007/s00415-019- 09518-3 · DOI
  10. Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: a review. Clin Exp Ophthalmol 2019; 47: 128- 39.https://doi.org/10.1111/ceo.13381 · DOI
  11. Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA- approved medical devices and Algorithms: An Online database. NPJ Digit Med 2020; 3: 118. https://doi.org/10.1038/s41746-020- 00324-0 · DOI · Paper page
  12. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-8.https:// doi.org/10.1038/nature21056 · DOI · Paper page
  13. Summers RM. Artificial intelligence of COVID-19 imaging: a hammer in search of a nail. Radiology 2021; 298: E162-4.https://doi. org/10.1148/radiol.2020204226 · DOI
  14. López-Cabrera JD, Orozco-Morales R, Portal-Diaz JA, Lovelle-Enríquez O, Pérez- Díaz M. Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. Health and Technology 2021; 11: 411-24.https://doi.org/10.1007/ s12553-021-00520-2 · DOI
  15. Luo Y, Tseng H-H, Cui S, Wei L, Ten Haken RK, El Naqa I, Haken RKT, Naqa IE. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR|Open 2019; 1: : 20190021.https://doi.org/10.1259/bjro. 20190021 · DOI
  16. Haupt C. Artificial professional advice. Yale Journal of Law & Technology 2019; 21: 55-77.
  17. Terry NP. Of regulating healthcare AI and robots. Yale Journal of Law & Technology 2019; 21: 133-90.
  18. Augmented intelligence in health care. 2018. Available from: https://www.ama-assn. org/system/files/2019-01/augmented- intelligence-policy-report.pdf.
  19. Froomkin M, Kerr I, Pineau J. When AIS Outperform doctors: confronting the challenges of a Tort-Induced Over-Reliance on machine learning. Arizona Law Review 2019; 61: 33-99.
  20. Griffin F. Artificial Intelligence and Liability in Health Care. Health Matrix: The Journal of Law-Medicine. 2021;31(1):65-106.
  21. Chipidza FE, Wallwork RS, Stern TA. Impact of the doctor-patient relationship. Prim Care Companion CNS Disord 2015; 17 .https://doi. org/10.4088/PCC.15f01840 · DOI
  22. Halpern J. Empathy and patient-physician conflicts. J Gen Intern Med 2007; 22: 696-700.https://doi.org/10.1007/s11606-006- 0102-3 · DOI
  23. Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Med Inform 2020; 8(7): e18599. https://doi.org/10. 2196/18599 · DOI · Paper page
  24. Nishida N, Kudo M. Artificial intelligence in medical imaging and its application in sonography for the management of liver tumor. Front Oncol 2020; 10: 594580.https:// doi.org/10.3389/fonc.2020.594580 · DOI
  25. Stone EG. Unintended adverse consequences of a clinical decision support system: two cases. Journal of the American Medical Informatics Association 2018; 25: 564-7.https://doi.org/10.1093/jamia/ocx096 · DOI
  26. Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bull World Health Organ 2020; 98: 251-6.https://doi.org/10.2471/BLT.19.237487 · DOI · Paper page
  27. Char DS, Abràmoff MD, Feudtner C. Identifying ethical considerations for machine learning healthcare applications. Am J Bioeth 2020; 20: 7-17.https://doi.org/ 10.1080/15265161.2020.1819469 · DOI · Paper page
  28. Balagurunathan Y, Mitchell R, El Naqa I. Requirements and reliability of AI in the medical context. Physica Medica 2021; 83: 72-8.https://doi.org/10.1016/j.ejmp.2021.02. 024 · DOI
  29. Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf 2019; 28: 231-7.https://doi.org/10. 1136/bmjqs-2018-008370 · DOI · Paper page
  30. Wright A, Hickman T-TT, McEvoy D, Aaron S, Ai A, Andersen JM, et al. Analysis of clinical decision support system malfunctions: a case series and survey. Journal of American Medical Informatics Association 2016; 23: 1068-76.https://doi. org/10.1093/jamia/ocw005 · DOI · Paper page
  31. Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, et al. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95: 20220239.https://doi.org/10.1259/bjr. 20220239 · DOI
  32. El Naqa I, Boone JM, Benedict SH, Goodsitt MM, Chan Heang-Ping, Drukker K, et al. Ai in medical physics: guidelines for publication. Med Phys 2021; 48: 4711-4.https://doi.org/10.1002/mp.15170 · DOI
  33. McIntosh C, Conroy L, Tjong MC, Craig T, Bayley A, Catton C, et al. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat Med 2021; 27: 999-1005.https:// doi.org/10.1038/s41591-021-01359-w · DOI
  34. El Naqa I. Prospective clinical deployment of machine learning in radiation oncology. Nat Rev Clin Oncol 2021; 18: 605-6.https://doi. org/10.1038/s41571-021-00541-w · DOI
  35. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad- CAM: Visual Explanations from Deep Networks via Gradient-based Localization. arXiv:1610.02391. 2016. Available from: https://ui.adsabs.harvard.edu/abs/ 2016arXiv161002391S (accessed 1 Oct 2016) · arXiv
  36. Ribeiro MT, Singh S, Guestrin C. Why Should I Trust You?": Explaining the Predictions of Any Classifier. In: Paper presented at the In: Paper presented at the In: Paper presented at the presented at: Proceedings of the 22nd ACM SIGKDD BJR Ethical and legal issues of Artificial Intelligence. International Conference on Knowledge Discovery and Data Mining; 2016, , San Francisco, California, USA. https://doi.org/ 10.1145/2939672.2939778 · DOI · Paper page
  37. Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. In: Paper presented at the In: Paper presented at the In: Paper presented at the presented at: Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017, , Long Beach, California, USA.
  38. Fuhrman JD, Gorre N, Hu Q, Li H, El Naqa I, Giger ML. A review of explainable and interpretable AI with applications in COVID-19 imaging. Med Phys 2022; 49: 1- 14.https://doi.org/10.1002/mp.15359 · DOI
  39. Luo Y, Jolly S, Palma D, Lawrence TS, Tseng H-H, Valdes G, et al. A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy outcomes prediction in lung cancer patients. Phys Med 2021; 87: 11-23.https://doi.org/10. 1016/j.ejmp.2021.05.032 · DOI
  40. Sun W, Niraula D, El Naqa I, Ten Haken RK, Dinov ID, Cuneo K, et al. Precision radiotherapy via information integration of expert human knowledge and AI recommendation to optimize clinical decision making. Comput Methods Programs Biomed 2022; 221: 106927.https://doi.org/10. 1016/j.cmpb.2022.106927 · DOI
  41. Niraula D, Jamaluddin J, Matuszak MM, Haken RKT, Naqa IE. Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy. Sci Rep 2021; 11: 23545.https:// doi.org/10.1038/s41598-021-02910-y · DOI
  42. Pakela JM, Matuszak MM, Ten Haken RK, McShan DL, El Naqa I. Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer. Phys. Med. Biol. 2021; 66: 225006.https://doi. org/10.1088/1361-6560/ac2b80 · DOI
  43. Gerke S, Minssen T, Cohen IG. Ethical and legal challenges of artificial intelligence- driven healthcare. In: Bohr A, Memarzadeh K, eds.Artificial Intelligence in Healthcare. Elsevier. 2020. pp. 295-336.
  44. Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health 2021; 3: e195-203.https://doi.org/10.1016/ S2589-7500(20)30292-2 · DOI · Paper page
  45. Forcier MB, Gallois H, Mullan S, Joly Y. Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers? J Law Biosci 2019; 6: 317-35.https://doi.org/10.1093/jlb/lsz013 · DOI
  46. Forti M. The deployment of artifical intelligence tools in the health sector: privacy concerns and regulatory answers in the GDPR. European Journal of Legal Studies 2021; 13: 29-44.
  47. Smith H, Fotheringham K. Artificial intelligence in clinical decision-making: rethinking liability. Med Law Int 2020; 20: 131-54.https://doi.org/10.1177/ 0968533220945766 · DOI
  48. Fan Z, Fu PP, Yu H, Ray PC. Theranostic nanomedicine for cancer detection and treatment. Journal of Food and Drug Analysis 2014; 22: 3-17.https://doi.org/10.1016/j.jfda. 2014.01.001 · DOI
  49. Shreyash N, Sonker M, Bajpai S, Tiwary SK. Review of the mechanism of nanocarriers and technological developments in the field of nanoparticles for applications in cancer Theragnostics. ACS Applied Bio Materials 2021; 4: 2307-34.https://doi.org/10.1021/ acsabm.1c00020 · DOI
  50. Degrauwe N, Hocquelet A, Digklia A, Schaefer N, Denys A, Duran R. Theranostics in interventional oncology: versatile carriers for diagnosis and targeted image-guided minimally invasive procedures. Front Pharmacol 2019; 10: 450.https://doi.org/10. 3389/fphar.2019.00450 · DOI
  51. Gomes Marin JF, Nunes RF, Coutinho AM, Zaniboni EC, Costa LB, Barbosa FG, et al. Theranostics in nuclear medicine: emerging and re-emerging integrated imaging and therapies in the era of precision oncology. Radiographics 2020; 40: 1715-40.https://doi. org/10.1148/rg.2020200021 · DOI
  52. Gurgitano M, Angileri SA, Rodà GM, Liguori A, Pandolfi M, Ierardi AM, et al. Interventional radiology ex-machina: impact of artificial intelligence on practice. Radiol Med 2021; 126: 998-1006.https://doi.org/10. 1007/s11547-021-01351-x · DOI · Paper page
  53. Seah J, Boeken T, Sapoval M, Goh GS. Prime time for artificial intelligence in interventional radiology. CardioVascular and Interventional Radiology 2022; 45: 283-9.https://doi.org/10.1007/s00270-021- 03044-4 · DOI

Cited by

Sorted by citations

Authors
Drabiak, K., Kyzer, S., Nemov, V., & El Naqa, I.
Affiliations
  • University of South Florida (US)
  • Department of Machine Learning, Moffitt Cancer Center , Tampa, FL, USA
ORCID
Venue
The British Journal of Radiology
Publisher
Oxford University Press (OUP)
Published
2023-05-23
Volume
96
Issue
1150
Pages
20220934-20220934
ISSN
  • 0007-1285 (ISSN-L)
  • 1748-880X
DOI
10.1259/bjr.20220934
PMC
PMC10546451
OpenAlex
W4376642144
Cited by
84
References
7
Citations by year
2026: 23 · 2025: 35 · 2024: 21 · 2023: 5
FWCI
0.99
Type
journal article
Language
en
Open access
Licence
academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations
  • British Journal of Radiology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Defense
  • University of South Florida
  • National Institutes of Health
  • Morsani College of Medicine
  • National Cancer Institute (R37CA222215, R01CA233487, R01CA266803)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
artificial intelligence in healthcare, machine learning ethics, patient privacy, legal responsibility, algorithmic bias, trustworthy AI, transparency, AI regulation, health law, patient trust
MeSH terms
Machine Learning, Algorithms, Artificial Intelligence, Europe, Humans, Medicine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review