Top

Published in:

Open Access 04-08-2023 | Artificial Intelligence | Invited Review

Fairness of artificial intelligence in healthcare: review and recommendations

Authors: Daiju Ueda, Taichi Kakinuma, Shohei Fujita, Koji Kamagata, Yasutaka Fushimi, Rintaro Ito, Yusuke Matsui, Taiki Nozaki, Takeshi Nakaura, Noriyuki Fujima, Fuminari Tatsugami, Masahiro Yanagawa, Kenji Hirata, Akira Yamada, Takahiro Tsuboyama, Mariko Kawamura, Tomoyuki Fujioka, Shinji Naganawa

Published in: Japanese Journal of Radiology | Issue 1/2024

Abstract

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: Mapping the debate. Big Data Soc. 2016;3:2053951716679679. SAGE Publications Ltd. https://doi.org/10.1177/2053951716679679

Jobin A, Ienca M, Vayena E. Artificial intelligence: the global landscape of ethics guidelines. Nat Mach Intell. 2019;1:389–99. https://doi.org/10.1038/s42256-019-0088-2.CrossRef

Tsamados A, Aggarwal N, Cowls J, Morley J, Roberts H, Taddeo M, et al. The ethics of algorithms: key problems and solutions. AI & Soc. 2022;37:215–30. https://doi.org/10.1007/s00146-021-01154-8.CrossRef

Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S. Human decisions and machine predictions. Q J Econ. 2018;133:237–93. https://doi.org/10.1093/qje/qjx032.CrossRefPubMed

Edwards V. Slave to the algorithm: Why a right to an explanation is probably not the remedy you are looking for. Duke Law Technol Rev. https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/dltr16&section=3.

Binns R. Fairness in machine learning: Lessons from political philosophy. In: Friedler SA, Wilson C, editors. Proceedings of the 1st conference on fairness, accountability and transparency. PMLR; 2018. p. 149–59

Selbst AD, Boyd D, Friedler SA, Venkatasubramanian S, Vertesi J. Fairness and abstraction in sociotechnical systems. In: Proceedings of the conference on fairness, accountability, and transparency. New York, USA. Association for Computing Machinery; 2019. https://doi.org/10.1145/3287560.3287598

Wong P-H. Democratizing algorithmic fairness. Philos Technol. 2020;33:225–44. https://doi.org/10.1007/s13347-019-00355-w.CrossRef

Abebe R, Barocas S, Kleinberg J, Levy K, Raghavan M, Robinson DG. Roles for computing in social change. In: Proceedings of the 2020 conference on fairness, accountability, and transparency. New York, USA. Association for Computing Machinery; 2020. https://doi.org/10.1145/3351095.3372871

10.

Bærøe K, Gundersen T, Henden E, Rommetveit K. Can medical algorithms be fair? Three ethical quandaries and one dilemma. BMJ Health Care Inform. 2022. https://doi.org/10.1136/bmjhci-2021-100445.CrossRefPubMedPubMedCentral

11.

Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018;169:866–72. https://doi.org/10.7326/M18-1990.CrossRefPubMedPubMedCentral

12.

Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Comput Surv. 2021;54:1–35. https://doi.org/10.1145/3457607.

13.

World Medical Association. Declaration of Geneva. World Medical Association; 1983

14.

Ueda D, Shimazaki A, Miki Y. Technical and clinical overview of deep learning in radiology. Jpn J Radiol. 2019;37:15–33. https://doi.org/10.1007/s11604-018-0795-3.CrossRefPubMed

15.

Yuba M, Iwasaki K. Systematic analysis of the test design and performance of AI/ML-based medical devices approved for triage/detection/diagnosis in the USA and Japan. Sci Rep. 2022;12:16874. https://doi.org/10.1038/s41598-022-21426-7.CrossRefPubMedPubMedCentral

16.

Zhu S, Gilbert M, Chetty I, The SF. landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: an analysis of the characteristics and intended use. Int J Med Inform. 2021. https://doi.org/10.1016/j.ijmedinf.2022.104828.CrossRefPubMedPubMedCentral

17.

Kelly BS, Judge C, Bollard SM, Clifford SM, Healy GM, Aziz A, et al. Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). Eur Radiol. 2022;32:7998–8007. https://doi.org/10.1007/s00330-022-08784-6.CrossRefPubMedPubMedCentral

18.

Rimmer A. Radiologist shortage leaves patient care at risk, warns royal college. BMJ. 2017. https://doi.org/10.1136/bmj.j4683.CrossRefPubMed

19.

Nakajima Y, Yamada K, Imamura K, Kobayashi K. Radiologist supply and workload: international comparison–working group of Japanese college of radiology. Radiat Med. 2008;26:455–65. https://doi.org/10.1007/s11604-008-0259-2.CrossRefPubMed

20.

Gourd EE. UK radiologist staffing crisis reaches critical levels. Lancet Oncol. 2017. https://doi.org/10.1016/S1470-2045(17)30806-9.CrossRefPubMed

21.

Mollura DJ, Culp MP, Pollack E, Battino G, Scheel JR, Mango VL, et al. Artificial intelligence in low- and middle-income countries: innovating global health radiology. Radiology. 2020;297:513–20. https://doi.org/10.1148/radiol.2020201434.CrossRefPubMed

22.

Toda N, Hashimoto M, Iwabuchi Y, Nagasaka M, Takeshita R, Yamada M, et al. Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers’ performance and final diagnosis. Jpn J Radiol. 2023;41:38–44. https://doi.org/10.1007/s11604-022-01330-w.CrossRefPubMed

23.

Ueda D, Ehara S, Yamamoto A, Iwata S, Abo K, Walston SL, et al. Development and validation of artificial intelligence-based method for diagnosis of mitral regurgitation from chest radiographs. Radiol Artif Intell. 2022. https://doi.org/10.1148/ryai.210221.CrossRefPubMedPubMedCentral

24.

Ueda D, Yamamoto A, Ehara S, Iwata S, Abo K, Walston SL, et al. Artificial intelligence-based detection of aortic stenosis from chest radiographs. Eur Heart J Digit Health. 2022;3:20–8. https://doi.org/10.1093/ehjdh/ztab102.CrossRefPubMed

25.

Matsumoto T, Ehara S, Walston SL, Mitsuyama Y, Miki Y, Ueda D. Artificial intelligence-based detection of atrial fibrillation from chest radiographs. Eur Radiol. 2022;32:5890–7. https://doi.org/10.1007/s00330-022-08752-0.CrossRefPubMed

26.

Ueda D, Yamamoto A, Takashima T, Onoda N, Noda S, Kashiwagi S, et al. Visualizing “featureless” regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiology. Jpn J Radiol. 2021;39:333–40. https://doi.org/10.1007/s11604-020-01070-9.CrossRefPubMed

27.

Uematsu T, Nakashima K, Harada TL, Nasu H, Igarashi T. Comparisons between artificial intelligence computer-aided detection synthesized mammograms and digital mammograms when used alone and in combination with tomosynthesis images in a virtual screening setting. Jpn J Radiol. 2023;41:63–70. https://doi.org/10.1007/s11604-022-01327-5.CrossRefPubMed

28.

Ueda D, Yamamoto A, Onoda N, Takashima T, Noda S, Kashiwagi S, et al. Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets. PLoS ONE. 2022. https://doi.org/10.1371/journal.pone.0265751.CrossRefPubMedPubMedCentral

29.

Honjo T, Ueda D, Katayama Y, Shimazaki A, Jogo A, Kageyama K, et al. Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution. Eur J Radiol. 2022. https://doi.org/10.1016/j.ejrad.2022.110433.CrossRefPubMed

30.

Ueda D, Yamamoto A, Takashima T, Onoda N, Noda S, Kashiwagi S, et al. Training, validation, and test of deep learning models for classification of receptor expressions in breast cancers from mammograms. JCO Precis Oncol. 2021;5:543–51. https://doi.org/10.1200/PO.20.00176.CrossRefPubMed

31.

Ozaki J, Fujioka T, Yamaga E, Hayashi A, Kujiraoka Y, Imokawa T, et al. Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography. Jpn J Radiol. 2022;40:814–22. https://doi.org/10.1007/s11604-022-01261-6.CrossRefPubMed

32.

Ichikawa Y, Kanii Y, Yamazaki A, Nagasawa N, Nagata M, Ishida M, et al. Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction. Jpn J Radiol. 2021;39:598–604. https://doi.org/10.1007/s11604-021-01089-6.CrossRefPubMed

33.

Nakai H, Fujimoto K, Yamashita R, Sato T, Someya Y, Taura K, et al. Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study. Jpn J Radiol. 2021;39:690–702. https://doi.org/10.1007/s11604-021-01106-8.CrossRefPubMed

34.

Okuma T, Hamamoto S, Maebayashi T, Taniguchi A, Hirakawa K, Matsushita S, et al. Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results. Jpn J Radiol. 2021;39:956–65. https://doi.org/10.1007/s11604-021-01134-4.CrossRefPubMedPubMedCentral

35.

Kitahara H, Nagatani Y, Otani H, Nakayama R, Kida Y, Sonoda A, et al. A novel strategy to develop deep learning for image super-resolution using original ultra-high-resolution computed tomography images of lung as training dataset. Jpn J Radiol. 2022;40:38–47. https://doi.org/10.1007/s11604-021-01184-8.CrossRefPubMed

36.

Kaga T, Noda Y, Mori T, Kawai N, Miyoshi T, Hyodo F, et al. Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: Image quality and anatomical structure depiction. Jpn J Radiol. 2022;40:703–11. https://doi.org/10.1007/s11604-022-01259-0.CrossRefPubMedPubMedCentral

37.

Ohno Y, Aoyagi K, Arakita K, Doi Y, Kondo M, Banno S, et al. Newly developed artificial intelligence algorithm for COVID-19 pneumonia: Utility of quantitative CT texture analysis for prediction of favipiravir treatment effect. Jpn J Radiol. 2022;40:800–13. https://doi.org/10.1007/s11604-022-01270-5.CrossRefPubMedPubMedCentral

38.

Matsukiyo R, Ohno Y, Matsuyama T, Nagata H, Kimata H, Ito Y, et al. Deep learning-based and hybrid-type iterative reconstructions for CT: Comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions. Jpn J Radiol. 2021;39:186–97. https://doi.org/10.1007/s11604-020-01045-w.CrossRefPubMed

39.

Koretsune Y, Sone M, Sugawara S, Wakatsuki Y, Ishihara T, Hattori C, et al. Validation of a convolutional neural network for the automated creation of curved planar reconstruction images along the main pancreatic duct. Jpn J Radiol. 2023;41:228–34. https://doi.org/10.1007/s11604-022-01339-1.CrossRefPubMed

40.

Anai K, Hayashida Y, Ueda I, Hozuki E, Yoshimatsu Y, Tsukamoto J, et al. The effect of CT texture-based analysis using machine learning approaches on radiologists’ performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol. 2022;40:1156–65. https://doi.org/10.1007/s11604-022-01298-7.CrossRefPubMedPubMedCentral

41.

Cay N, Mendi BAR, Batur H, Erdogan F. Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning. Jpn J Radiol. 2022;40:951–60. https://doi.org/10.1007/s11604-022-01278-x.CrossRefPubMed

42.

Wong LM, Ai QYH, Mo FKF, Poon DMC, King AD. Convolutional neural network in nasopharyngeal carcinoma: How good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI? Jpn J Radiol. 2021;39:571–9. https://doi.org/10.1007/s11604-021-01092-x.CrossRefPubMed

43.

Yasaka K, Akai H, Sugawara H, Tajima T, Akahane M, Yoshioka N, et al. Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography. Jpn J Radiol. 2022. https://doi.org/10.1007/s11604-021-01225-2.CrossRefPubMed

44.

Nomura Y, Hanaoka S, Nakao T, Hayashi N, Yoshikawa T, Miki S, et al. Performance changes due to differences in training data for cerebral aneurysm detection in head MR angiography images. Jpn J Radiol. 2021;39:1039–48. https://doi.org/10.1007/s11604-021-01153-1.CrossRefPubMed

45.

Ishihara M, Shiiba M, Maruno H, Kato M, Ohmoto-Sekine Y, Antoine C, et al. Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN’s final layer for distinguishing between aneurysm and infundibular dilatation. Jpn J Radiol. 2023;41:131–41. https://doi.org/10.1007/s11604-022-01341-7.CrossRefPubMed

46.

Miki S, Nakao T, Nomura Y, Okimoto N, Nyunoya K, Nakamura Y, et al. Computer-aided detection of cerebral aneurysms with magnetic resonance angiography: usefulness of volume rendering to display lesion candidates. Jpn J Radiol. 2021;39:652–8. https://doi.org/10.1007/s11604-021-01099-4.CrossRefPubMed

47.

Nakaura T, Kobayashi N, Yoshida N, Shiraishi K, Uetani H, Nagayama Y, et al. Update on the use of artificial intelligence in hepatobiliary MR imaging. Magn Reson Med Sci. 2023;22:147–56. https://doi.org/10.2463/mrms.rev.2022-0102.CrossRefPubMedPubMedCentral

48.

Naganawa S, Ito R, Kawai H, Kawamura M, Taoka T, Sakai M, et al. MR imaging of endolymphatic hydrops in five minutes. Magn Reson Med Sci. 2022;21:401–5. https://doi.org/10.2463/mrms.ici.2021-0022.CrossRefPubMed

49.

Kabasawa H, Kiryu S. Pulse sequences and reconstruction in Fast MR imaging of the liver. Magn Reson Med Sci. 2023;22:176–90. https://doi.org/10.2463/mrms.rev.2022-0114.CrossRefPubMedPubMedCentral

50.

Iwamura M, Ide S, Sato K, Kakuta A, Tatsuo S, Nozaki A, et al. Thin-slice two-dimensional T2-weighted imaging with deep learning-based reconstruction: Improved lesion detection in the brain of patients with multiple sclerosis. Magn Reson Med Sci. 2023. https://doi.org/10.2463/mrms.mp.2022-0112.CrossRefPubMed

51.

Nai YH, Loi HY, O’Doherty S, Tan TH, Reilhac A. Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images. Jpn J Radiol. 2022;40:1290–9. https://doi.org/10.1007/s11604-022-01311-z.CrossRefPubMed

52.

Nakao T, Hanaoka S, Nomura Y, Hayashi N, Abe O. Anomaly detection in chest 18F-FDG PET/CT by bayesian deep learning. Jpn J Radiol. 2022;40:730–9. https://doi.org/10.1007/s11604-022-01249-2.CrossRefPubMedPubMedCentral

53.

Kumamaru KK, Machitori A, Koba R, Ijichi S, Nakajima Y, Aoki S. Global and Japanese regional variations in radiologist potential workload for computed tomography and magnetic resonance imaging examinations. Jpn J Radiol. 2018;36:273–81. https://doi.org/10.1007/s11604-018-0724-5.CrossRefPubMed

54.

Cozzi D, Cavigli E, Moroni C, Smorchkova O, Zantonelli G, Pradella S, et al. Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19. Jpn J Radiol. 2021;39:721–32. https://doi.org/10.1007/s11604-021-01120-w.CrossRefPubMedPubMedCentral

55.

Aoki R, Iwasawa T, Hagiwara E, Komatsu S, Utsunomiya D, Ogura T. Pulmonary vascular enlargement and lesion extent on computed tomography are correlated with COVID-19 disease severity. Jpn J Radiol. 2021;39:451–8. https://doi.org/10.1007/s11604-020-01085-2.CrossRefPubMedPubMedCentral

56.

Zhu QQ, Gong T, Huang GQ, Niu ZF, Yue T, Xu FY, et al. Pulmonary artery trunk enlargement on admission as a predictor of mortality in in-hospital patients with COVID-19. Jpn J Radiol. 2021;39:589–97. https://doi.org/10.1007/s11604-021-01094-9.CrossRefPubMedPubMedCentral

57.

Fukuda A, Yanagawa N, Sekiya N, Ohyama K, Yomota M, Inui T, et al. An analysis of the radiological factors associated with respiratory failure in COVID-19 pneumonia and the CT features among different age categories. Jpn J Radiol. 2021;39:783–90. https://doi.org/10.1007/s11604-021-01118-4.CrossRefPubMedPubMedCentral

58.

Özer H, Kılınçer A, Uysal E, Yormaz B, Cebeci H, Durmaz MS, et al. Diagnostic performance of radiological society of North America structured reporting language for chest computed tomography findings in patients with COVID-19. Jpn J Radiol. 2021;39:877–88. https://doi.org/10.1007/s11604-021-01128-2.CrossRefPubMedPubMedCentral

59.

Zhuang Y, Lin L, Xu X, Xia T, Yu H, Fu G, et al. Dynamic changes on chest CT of COVID-19 patients with solitary pulmonary lesion in initial CT. Jpn J Radiol. 2021;39:32–9. https://doi.org/10.1007/s11604-020-01037-w.CrossRefPubMed

60.

Kanayama A, Tsuchihashi Y, Otomi Y, Enomoto H, Arima Y, Takahashi T, et alAssociation of severe COVID-19 outcomes with radiological scoring and cardiomegaly: Findings from the COVID-19 inpatients database, Japan. Jpn J Radiol. 2022 https://doi.org/10.1007/s11604-022-01300-2

61.

Inui S, Fujikawa A, Gonoi W, Kawano S, Sakurai K, Uchida Y, et al. Comparison of CT findings of coronavirus disease 2019 (COVID-19) pneumonia caused by different major variants. Jpn J Radiol. 2022;40:1246–56. https://doi.org/10.1007/s11604-022-01301-1.CrossRefPubMedPubMedCentral

62.

Walston SL, Matsumoto T, Miki Y, Ueda D. Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study. Br J Radiol. 2022;95:20220058. https://doi.org/10.1259/bjr.20220058.CrossRefPubMedPubMedCentral

63.

Matsumoto T, Walston SL, Walston M, Kabata D, Miki Y, Shiba M, et al. Deep learning-based time-to-death prediction model for COVID-19 patients using clinical data and chest radiographs. J Digit Imaging. 2023;36:178–88. https://doi.org/10.1007/s10278-022-00691-y.CrossRefPubMed

64.

Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of Covid-19: Systematic review and critical appraisal. BMJ. 2020;369:m1328. https://doi.org/10.1136/bmj.m1328.CrossRefPubMedPubMedCentral

65.

Marmot M, Bell R. Fair society, healthy lives. Public Health. 2012;126(Suppl 1):S4–10. https://doi.org/10.1016/j.puhe.2012.05.014.CrossRefPubMed

66.

Ricci Lara MA, Echeveste R, Ferrante E. Addressing fairness in artificial intelligence for medical imaging. Nat Commun. 2022;13:4581. https://doi.org/10.1038/s41467-022-32186-3.CrossRefPubMedPubMedCentral

67.

Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H 3rd, Iii HD, et al. Datasheets for datasets. Commun ACM. 2021. New York: Association for Computing Machinery;64:86–92. https://doi.org/10.1145/3458723

68.

Wenger NK. Women and coronary heart disease: A century after Herrick: Understudied, underdiagnosed, and undertreated. Circulation. 2012;126:604–11. https://doi.org/10.1161/CIRCULATIONAHA.111.086892.CrossRefPubMed

69.

Appelman Y, van Rijn BB, Ten Haaf ME, Boersma E, Peters SAE. Sex differences in cardiovascular risk factors and disease prevention. Atherosclerosis. 2015;241:211–8. https://doi.org/10.1016/j.atherosclerosis.2015.01.027.CrossRefPubMed

70.

Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol. 2018;154:1247–8. https://doi.org/10.1001/jamadermatol.2018.2348.CrossRefPubMed

71.

Navarrete-Dechent C, Dusza SW, Liopyris K, Marghoob AA, Halpern AC, Marchetti MA. Automated dermatological diagnosis: hype or reality? J Invest Dermatol. 2018;138:2277–9. https://doi.org/10.1016/j.jid.2018.04.040.CrossRefPubMedPubMedCentral

72.

Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLOS Med. 2018. https://doi.org/10.1371/journal.pmed.1002683.CrossRefPubMedPubMedCentral

73.

Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170:51–8. https://doi.org/10.7326/M18-1376.CrossRefPubMed

74.

Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286:800–9. https://doi.org/10.1148/radiol.2017171920.CrossRefPubMed

75.

Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology. 2020;294:487–9. https://doi.org/10.1148/radiol.2019192515.CrossRefPubMed

76.

Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366:447–53. https://doi.org/10.1126/science.aax2342.CrossRefPubMed

77.

Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003;129:674–97. https://doi.org/10.1037/0033-2909.129.5.674.CrossRefPubMedPubMedCentral

78.

Anderson M, Anderson SL. How should AI be developed, validated, and implemented in patient care? AMA J Ethics. Am Med Assoc. 2019;21:E125-130. https://doi.org/10.1001/amajethics.2019.125.CrossRef

79.

Dratsch T, Chen X, Rezazade Mehrizi M, Kloeckner R, Mähringer-Kunz A, Püsken M, et al. Automation bias in mammography: The impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology. 2023. https://doi.org/10.1148/radiol.222176.CrossRefPubMed

80.

Walsh CG, Chaudhry B, Dua P, Goodman KW, Kaplan B, Kavuluru R, et al. Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence. JAMIA Open. 2020;3:9–15. https://doi.org/10.1093/jamiaopen/ooz054.CrossRefPubMedPubMedCentral

81.

Lehman CD, Wellman RD, Buist DSM, Kerlikowske K, Tosteson ANA, Miglioretti DL, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015;175:1828–37. https://doi.org/10.1001/jamainternmed.2015.5231.CrossRefPubMedPubMedCentral

82.

Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, Teich JM, et al. Drug—drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc Oxford. 2012. Academic Press;20:489–93

83.

Fiscella K, Williams DR. Health disparities based on socioeconomic inequities: implications for urban health care. Acad Med. 2004;79:1139–47. https://doi.org/10.1097/00001888-200412000-00004.CrossRefPubMed

84.

Gamble VN. Under the shadow of Tuskegee: African Americans and health care. Am J Public Health. 1997;87:1773–8. https://doi.org/10.2105/ajph.87.11.1773.CrossRefPubMedPubMedCentral

85.

Boulware LE, Cooper LA, Ratner LE, LaVeist TA, Powe NR. Race and trust in the health care system. Public Health Rep. 2003;118:358–65. https://doi.org/10.1093/phr/118.4.358.CrossRefPubMedPubMedCentral

86.

Minkler M, Wallerstein N. Community-based participatory research for health: From process to outcomes. John Wiley & Sons; 2011

87.

Haibe-Kains B, Adam GA, Hosny A, Khodakarami F, Massive Analysis Quality Control (MAQC) Society Board of Directors, Waldron L, et al. Transparency and reproducibility in artificial intelligence. Nature. 2020. p. E14–6

88.

Buolamwini J, Gebru T. Gender shades: Intersectional accuracy disparities in commercial gender classification. In: Friedler SA, Wilson C, editors. Proceedings of the 1st conference on fairness, accountability and transparency. PMLR; 2018. p. 77–91

89.

Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, et al. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385:283–6. https://doi.org/10.1056/NEJMc2104626.CrossRefPubMedPubMedCentral

90.

Feng J, Phillips RV, Malenica I, Bishara A, Hubbard AE, Celi LA, et al. Clinical artificial intelligence quality improvement: Towards continual monitoring and updating of AI algorithms in healthcare. npj Digit Med. 2022. https://doi.org/10.1038/s41746-022-00611-y.CrossRefPubMedPubMedCentral

91.

Cummings ML. Automation bias in intelligent time critical decision support systems. Decision making in aviation Routledge. 2004. https://doi.org/10.2514/6.2004-6313.CrossRef

92.

Gafni A, Charles C, McMaster University. Centre for health economics and policy analysis, Whelan T. Shared decision-making in the medical encounter: What does it mean?, or, it takes at least two to tango. Centre for Health Economics and Policy Analysis, McMaster University; 1994.

93.

Coulter A, Collins A. Making shared decision-making a reality: No decision about me, without me. London: The King’s Fund; 2011;621.

94.

Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLOS Med. 2018. https://doi.org/10.1371/journal.pmed.1002689.CrossRefPubMedPubMedCentral

95.

Price WN 2nd, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25:37–43. https://doi.org/10.1038/s41591-018-0272-7.CrossRefPubMedPubMedCentral

96.

Grady C. Enduring and emerging challenges of informed consent. N Engl J Med. 2015;372:2172. https://doi.org/10.1056/NEJMc1503813.CrossRefPubMed

97.

Emanuel EJ, Wendler D, Grady C. What makes clinical research ethical? JAMA. 2000;283:2701–11. https://doi.org/10.1001/jama.283.20.2701.CrossRefPubMed

98.

Abouelmehdi K, Beni-Hessane A, Khaloufi H. Big healthcare data: Preserving security and privacy. J Big Data. 2018;5:1–18. https://doi.org/10.1186/s40537-017-0110-7.CrossRef

99.

Taylor L, Floridi L, van der Sloot B. Group privacy: New challenges of data technologies. Springer; 2016

100.

Neri E, Coppola F, Miele V, Bibbolino C, Grassi R. Artificial intelligence: who is responsible for the diagnosis? Radiol Med. 2020;125:517–21. https://doi.org/10.1007/s11547-020-01135-9.CrossRefPubMed

101.

Price WN 2nd, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA. 2019;322:1765–6. https://doi.org/10.1001/jama.2019.15064.CrossRefPubMed

102.

van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal. 2022. https://doi.org/10.1016/j.media.2022.102470.CrossRefPubMed

103.

Goebel R, Chander A, Holzinger K, Lecue F, Akata Z, Stumpf S, et al. Explainable AI: The new 42? Machine Learning and Knowledge Extraction. Springer International Publishing. 2018;295–303. https://doi.org/10.1007/978-3-319-99740-7_21

104.

Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L. Explaining explanations: An overview of interpretability of machine learning [Internet]; 2018. cs.AI. http://arxiv.org/abs/1806.00069. https://doi.org/10.1109/DSAA.2018.00018

105.

Rudin C. Stop explaining Black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1:206–15. https://doi.org/10.1038/s42256-019-0048-x.CrossRefPubMedPubMedCentral

106.

Miller T. Explanation in artificial intelligence: Insights from the social sciences. Artif Intell. 2019;267:1–38. https://doi.org/10.1016/j.artint.2018.07.007.CrossRef

107.

Lipton ZC. The mythos of model interpretability. In: machine learning, the concept of interpretability is both important and slippery. Queueing Syst. 2018. Association for Computing Machinery;16:31–57. https://doi.org/10.1145/3236386.3241340

108.

Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25:44–56. https://doi.org/10.1038/s41591-018-0300-7.CrossRefPubMed

109.

Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347–58. https://doi.org/10.1056/NEJMra1814259.CrossRefPubMed

110.

Obermeyer Z, Emanuel EJ. Predicting the future - Big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216–9. https://doi.org/10.1056/NEJMp1606181.CrossRefPubMedPubMedCentral

111.

Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018;36:257–72. https://doi.org/10.1007/s11604-018-0726-3.CrossRefPubMed

112.

Burrell J. How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data Soc. 2016;3:2053951715622512. SAGE Publications Ltd;3. https://doi.org/10.1177/2053951715622512

113.

Char DS, Shah NH, Magnus D. Implementing machine learning in health care - Addressing ethical challenges. N Engl J Med. 2018;378:981–3. https://doi.org/10.1056/NEJMp1714229.CrossRefPubMedPubMedCentral

114.

Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: a mapping review. Soc Sci Med. 2020. https://doi.org/10.1016/j.socscimed.2020.113172.CrossRefPubMed

115.

Epstein RM, Fiscella K, Lesser CS, Stange KC. Why the nation needs a policy push on patient-centered health care. Health Aff (Millwood). 2010;29:1489–95. https://doi.org/10.1377/hlthaff.2009.0888.CrossRefPubMed

116.

Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion. 2020;58:82–115. https://doi.org/10.1016/j.inffus.2019.12.012.CrossRef

117.

Vayena E, Dzenowagis J, Brownstein JS, Sheikh A. Policy implications of big data in the health sector. Bull World Health Organ. 2018;96:66–8. https://doi.org/10.2471/BLT.17.197426.CrossRefPubMed

118.

Mittelstadt BD, Floridi L. The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics. 2016;22:303–41. https://doi.org/10.1007/s11948-015-9652-2.CrossRefPubMed

119.

Coulter A, Ellins J. Effectiveness of strategies for informing, educating, and involving patients. BMJ. 2007;335:24–7. https://doi.org/10.1136/bmj.39246.581169.80.CrossRefPubMedPubMedCentral

120.

Open AI. GPT-4 technical report [Internet]. arXiv [cs.CL]; 2023. http://arxiv.org/abs/2303.08774

121.

Eloundou T, Manning S, Mishkin P, Rock D. GPTs are GPTs: An early look at the labor market impact potential of large language models [Internet]. arXiv; 2023. econ.GN. http://arxiv.org/abs/2303.10130

122.

Ueda D, Walston SL, Matsumoto T, Deguchi R, Tatekawa H, Miki Y. Evaluating GPT-4-based ChatGPT’s clinical potential on the NEJM quiz [Internet]; 2023. medRxiv. https://www.medrxiv.org/content/10.1101/2023.05.04.23289493v1

123.

Davis MA, Lim N, Jordan J, Yee J, Gichoya JW, Lee R. Imaging artificial intelligence: A framework for radiologists to address health equity, from the AJR special series on DEI. AJR Am J Roentgenol. 2023. American Roentgen Ray Society. https://doi.org/10.2214/AJR.22.28802

124.

Shimazaki A, Ueda D, Choppin A, Yamamoto A, Honjo T, Shimahara Y, et al. Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method. Sci Rep. 2022;12:727. https://doi.org/10.1038/s41598-021-04667-w.CrossRefPubMedPubMedCentral

125.

Ueda D, Yamamoto A, Nishimori M, Shimono T, Doishita S, Shimazaki A, et al. Deep learning for MR angiography: automated detection of cerebral aneurysms. Radiology. 2019;290:187–94. https://doi.org/10.1148/radiol.2018180901.CrossRefPubMed

126.

Ueda D, Yamamoto A, Shimazaki A, Walston SL, Matsumoto T, Izumi N, et al. Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study. BMC Cancer. 2021;21:1120. https://doi.org/10.1186/s12885-021-08847-9.CrossRefPubMedPubMedCentral

Title: Fairness of artificial intelligence in healthcare: review and recommendations
Authors: Daiju Ueda
Taichi Kakinuma
Shohei Fujita
Koji Kamagata
Yasutaka Fushimi
Rintaro Ito
Yusuke Matsui
Taiki Nozaki
Takeshi Nakaura
Noriyuki Fujima
Fuminari Tatsugami
Masahiro Yanagawa
Kenji Hirata
Akira Yamada
Takahiro Tsuboyama
Mariko Kawamura
Tomoyuki Fujioka
Shinji Naganawa
Publication date: 04-08-2023
Publisher: Springer Nature Singapore
Keyword: Artificial Intelligence
Published in: Japanese Journal of Radiology / Issue 1/2024
Print ISSN: 1867-1071
Electronic ISSN: 1867-108X
DOI: https://doi.org/10.1007/s11604-023-01474-3

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Fairness of artificial intelligence in healthcare: review and recommendations

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 1/2024

Diagnostic potential of [18F]FDG PET/MRI in non-small cell lung cancer lymph node metastasis: a meta-analysis

Inflammation index predicts radiation-induced lung injury and prognosis in lung tumors treated with stereotactic body radiation therapy

Transvaginal approach combined intracavitary and interstitial brachytherapy assisted by transrectal ultrasound: results from 30 patients with locally advanced cervical cancer

FAIR: a recipe for ensuring fairness in healthcare artificial intelligence

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

Utility of machine learning for identifying stapes fixation on ultra-high-resolution CT