Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
51 tokens/sec
GPT-4o
60 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
8 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles (2304.11530v4)

Published 23 Apr 2023 in cs.AI

Abstract: AI methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients. AI-based computer-assisted diagnosis and treatment tools can democratize healthcare by matching the clinical level or surpassing clinical experts. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can uncover complex relations in the data from a large set of inputs and even lead to new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. Here, we emphasize recent advances in AI-assisted medical image analysis, existing standards, and the significance of comprehending ethical issues and best practices for clinical settings. We cover the technical and ethical challenges and implications of deploying AI in hospitals and public organizations. We also discuss key measures and techniques to address ethical challenges, data scarcity, racial bias, lack of transparency, and algorithmic bias and provide recommendations and future directions.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. He, J. et al. The practical implementation of artificial intelligence technologies in medicine. \JournalTitleNature medicine 25, 30–36 (2019).
  2. Simonite, T. Google’s ai eye doctor gets ready to go to work in india. wired magazine. june 8, 2017 (2018).
  3. Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. \JournalTitleNature Biomedical Engineering 2, 158 (2018).
  4. Pivotal trial of an autonomous ai-based diagnostic system for detection of diabetic retinopathy in primary care offices. \JournalTitleNPJ digital medicine 1, 1–8 (2018).
  5. Medtronic. Gi genius™ intelligent endoscopy module (2023). Accessed: 2023-09-16.
  6. McKinney, S. M. et al. International evaluation of an ai system for breast cancer screening. \JournalTitleNature 577, 89–94 (2020).
  7. computer-assisted diagnostic software for lesions suspicious for cancer (2023). Accessed: 2023-09-16.
  8. Ouyang, D. et al. Video-based ai for beat-to-beat assessment of cardiac function. \JournalTitleNature 580, 252–256 (2020).
  9. Studio, C. Clinical software to unify healthcare data (2023). Accessed: 2023-09-16.
  10. Health, G. Dermassist (2023). Accessed: 2023-09-16.
  11. Health, G. The value of genomic analysis (2023). Accessed: 2023-09-16.
  12. Health, G. Google health research publications (2023). Accessed: 2023-09-16.
  13. The state of artificial intelligence-based fda-approved medical devices and algorithms: an online database. \JournalTitleNPJ digital medicine 3, 1–8 (2020).
  14. Centers for medicare & medicaid services. medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and final policy changes and fiscal year 2021 rates; quality reporting and medicare and medicaid promoting interoperability programs requirements for eligible hospitals and critical access hospitals. \JournalTitleFed. Regist. 85 3, 58432––59107 (2020).
  15. Artificial intelligence: healthcare’s new nervous system. \JournalTitleAI: Healthcare’s new nervous system (2017).
  16. Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare, 295–336 (2020).
  17. Thirty years of artificial intelligence in medicine (aime) conferences: A review of research themes. \JournalTitleArtificial intelligence in medicine 65, 61–73 (2015).
  18. Luxton, D. D. Recommendations for the ethical use and design of artificial intelligent care providers. \JournalTitleArtificial intelligence in medicine 62, 1–10 (2014).
  19. Rigby, M. J. Ethical dimensions of using artificial intelligence in health care. \JournalTitleAMA Journal of Ethics 21, 121–124 (2019).
  20. European union regulations on algorithmic decision-making and a “right to explanation”. \JournalTitleAI magazine 38, 50–57 (2017).
  21. Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the us. \JournalTitleHuman Factors in Healthcare 2, 100021 (2022).
  22. Merritt, S. M. et al. Automation-induced complacency potential: Development and validation of a new scale. \JournalTitleFrontiers in psychology 10, 225 (2019).
  23. When machines think: radiology’s next frontier. \JournalTitleRadiology 285, 713–718 (2017).
  24. Tennøe, T. Artificial Intelligence – Opportunities, Challenges and a Plan for Norway (2018).
  25. Association, W. M. et al. World medical association declaration of helsinki. ethical principles for medical research involving human subjects. \JournalTitleBulletin of the World Health Organization 79, 373 (2001).
  26. Guidance, W. Ethics and governance of artificial intelligence for health. \JournalTitleWorld Health Organization (2021).
  27. Straw, I. The automation of bias in medical artificial intelligence (ai): Decoding the past to create a better future. \JournalTitleArtificial intelligence in medicine 110, 101965 (2020).
  28. Sex bias in the diagnosis of borderline personality disorder and posttraumatic stress disorder. \JournalTitleProfessional Psychology: Research and Practice 25, 55 (1994).
  29. Bias in, bias out: Underreporting and underrepresentation of diverse skin types in machine learning research for skin cancer detection—a scoping review. \JournalTitleJournal of the American Academy of Dermatology (2021).
  30. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review. \JournalTitleJAMA dermatology 157, 1362–1369 (2021).
  31. Addressing artificial intelligence bias in retinal diagnostics. \JournalTitleTranslational Vision Science & Technology 10, 13–13 (2021).
  32. Smuha, N. A. The eu approach to ethics guidelines for trustworthy artificial intelligence. \JournalTitleComputer Law Review International 20, 97–106 (2019).
  33. Group, I. S. W. et al. Software as a medical device (samd): key definitions (2013).
  34. Hansson, S. O. Philosophy of medical technology. In Philosophy of Technology and Engineering Sciences, 1275–1300 (2009).
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (13)
  1. Debesh Jha (78 papers)
  2. Ashish Rauniyar (6 papers)
  3. Abhiskek Srivastava (1 paper)
  4. Desta Haileselassie Hagos (8 papers)
  5. Nikhil Kumar Tomar (25 papers)
  6. Vanshali Sharma (9 papers)
  7. Elif Keles (22 papers)
  8. Zheyuan Zhang (61 papers)
  9. Ugur Demir (18 papers)
  10. Ahmet Topcu (2 papers)
  11. Anis Yazidi (30 papers)
  12. Jan Erik Håakegård (1 paper)
  13. Ulas Bagci (154 papers)
Citations (6)