Full Reference List

• Meskó, B., Görög, M. A short guide for medical professionals in the era of artificial intelligence. npj Digit. Med. 3, 126 (2020).
• Parikh RB, Teeple S, Navathe AS. Addressing Bias in Artificial Intelligence in Health Care. JAMA 2019;322(24):2377-78.
• Vokinger KN, Feuerriegel S, Kesselheim A. Mitigating bias in machine learning for medicine. Communications Medicine 2021;1(25):1-3.
• Abràmoff MD, Tobey D, Char DS. Lessons learned about autonomous AI: Finding a safe, efficacious, and ethical path through the development process. Am J Ophthalmol 2020;214:134-142.
• Aggarwal R, Farag S, Martin G, Ashrafian H, Darzi A. Patient perceptions on data sharing and applying artificial intelligence to health care data: Cross-sectional survey. J Med Internet Res 2021;23(8)e26162.
• Pierson E, Culter DM, Leskovec J, Mullainathan S, Obermeyer Z. An algorithmic approach to reducing unexplained pain disparities. Nature Medicine 2021;27(1):136-140.
• Moran-Thomas A. Oximeters used to be designed for equity. What happened? Wired. 4 Jun 2021 (cited 8 Apr 2022).
• FDA. Pulse oximeters premarket notification submissions [510k(s)]: Guidance for Industry and Food and Drug Administration staff. 4 Mar 2013 (cited 8 Apr 2022).
• Moran-Thomas, A. How a popular medical device encodes racial bias. Boston Review. 5 Aug 2020 (cited 8 Apr 2022).
• Singh K, et al. Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19. Ann Am Thorac Soc. 2021 Jul;18(7):1129-1137.
• Ross C. Hospitals are using AI to predict the decline of Covid-19 patients - before knowing it works. STAT. 24 Apr 2020. Cited 4 Apr 2022.
• Drees J. Epic pays hospitals that use its EHR algorithms, report finds. Beckers Hospital Review. 26 Jul 2021. Cited 4 Apr 2022.
• Pierson E, Culter DM, Leskovec J, Mullainathan S, Obermeyer Z. An algorithmic approach to reducing unexplained pain disparities. Nature Medicine 2021;27(1):136-140.
• Robins R. AI systems are worse at diagnosing disease whent training data is skewed by sex. STAT. 25 May 2020. Cited 4 Apr 2022.
• Suleyman M, King D. Using AI to give doctors a 48-hour head started on life-threatning illness. DeepMind. 31 Jul 2019. Cited 4 Apr 2022.
• Tomašev N et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 2019;572(7767):116-119.
• Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthcare Management Forum. 2020;33(1):10-18.
• Panch T, Mattie H, Atun R. Artificial intelligence and algorthmic bias: imiplications for health systems. JOGH. 2019;9(2):1-5.
• Brault N Saxena M. For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth. J Eval Clin Pract. 2021;27:513-519.
• Kahneman D, Sibony O, Sunstein CR. Noise: A flaw in human judgment. New York: Little Brown Spark; 2021. 464p.
• Nelson GS. Bias in artificial intelligence. NCMJ. 2019. 80(4):220-222
• Canales C, Lee C, Cannesson M.Science without conscience is but the ruin of the soul: The ethics of big data and artificial intelligence in perioperative medicine. Anesth Analg. 2020; 130(5):1234-1243.
• Butt S, Butt H, Gnanappiragasam. Unintentional consequences of artificial intelligence in dermatology for patients with skin of colour. Clinical and Experimental Dermatology. 2021; 46:1333-1334.
• Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing artificial intelligence for clinical decision-making. Frontiers in Digital Health. 2021; 3:1-9.
• Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atansova K. Artificial intelligence, bias and clinical safety. BMJ Qual saf. 2019;28:231-237.
• DeCamp M, Lindvall C. Latent bias and the implementation of artificial intelligence in medicine. JAMIA. 2020;27(12):2020-2023.
• Pot M, Kieusseyan N, Prainsack B. Not all biases are bad: equitable and inequitable biases in machine learning and radiology. Insights Imaging. 2021;12(1):13.
• Byrne MD. Reducing Bias in Healthcare Artificial Intelligence. Journal of PeriAnesethesia Nursing. 2021;36:313-316.
• Seyyed L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021; 27:2176-2182.
• Baird A, Schuller B. Considerations for a more ethical approach to data in AI: On data representation and infrastructure. Front Big Data. 2020; 3:25.
• Scott I, Carter S, Coiera E. Clinician checklist for assessing suitability of machine learning applications in healthcare. BMJ Health Care Inform. 2021;28:e100251.
• Johnson-Mann CN, Loftus TJ, Bihorac A. Equity and artificial intelligence in surgical care. JAMA Surg. 2021;156(6):509-510.
• Esmaeilzadeh P. Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives. BMC Medical Informatics and Decision. 2020;20:170.
• Clarke AC. Profiles of the future: An Inquiry into the limits of the possible. London: Orion Pub Co; 2000. 256p.
• Nundy S, Montgomery T, Wachter RM. Promoting trust between patients and physicians in the era of artificial intelligence. JAMA. 2019;322(6):497-498.
• Klugman CM. Black boxes and bias in AI challenge autonomy. The American Journal of Bioethics.
• Kundu S. How will artificial intelligence change medical training? Communications Medicine. 2021; 1:8.
• Bates DW, Auerback A, Schulam P, Wright A, Aria S. Reporting and implementing interventions involving machine learning and artificial intelligence. Ann Intern Med. 2020;127:S137-S144.
• Char DS, Abràmoff MD, Feudtner C. Identifying ethical considerations for machine learning healthcare applications. Am J Bioeth. 2020; 20(11):7-17.
• Asan O, Emrah Bayrak A, Choudhury A. Artificial intelligence and human trust in healthcare: Focus on clinicians. J Med Internet Res. 2020;22(6):15154.
• Chauhan C, Gullaplli RR. Ethics of AI in pathology: current paradigms and emerging issues. AJP. 2021; 191(10):1673-1683.
• Nordling L. Mind the gap [Internet]. Nature. 2019 [cited 2022 Jun 1];573:5103-5105. Available from: https://media.nature.com/original/magazine-assets/d41586-019-02872-2/d41586-019-02872-2.pdf
• Christou CD, Tsouflas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol. 2021; 27(37):6191-6223.
• Cath C. Governing artificial intelligence:ethical, legal and technical opportunities and challenges. Phi Trans R Soc A. 2018; 376(2133):20180080.
• McCradden MD, Joshi S, Mazwi M, Anderson JA. Ethical limitations of algorithmic fairness solutions in health care machine learning. Lancet Digit Health. 2020;2(5):e221-e223.
• Leslie D, Mazumer A, Peppin A, Wolters MK, Hagerty A. Does "AI" stand for augmenting inequality in the era of covid-19 healthcare? BMJ. 2021;372:n304.
• Zou J, Schiebinger L. Ensuring that biomedical AI benefits diverse populations. EBioMedicine. 2021; 67: 103358.
• Nabi J. How bioethics can shape artificial intelligence and machine learning. Hastings Center Report. 2018;48(5):10-13.
• Richardson JP, Smith C, Cutris S, Watson S, Zhu X, Bary B, Sharp RR. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med. 2021;4(1):140.
• Gearhart A, Gaffar S, Change AC. A primer on artificial intelligence for the paediatric cardiologist. Cardiology in the Young. 2020; 30(7):934-945.
• Norori N, Hu Q, Marcelle Aellen F, Dalia Faraci F, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns (N Y). 2021;2(10):100347.