Email this article Problematic aspects of medical artificial intelligence. Part 2
- Authors: Бердутин В.А.1
-
Affiliations:
- ФБУЗ ПОМЦ ФМБА России
- Section: Reviews
- URL: https://rjsocmed.com/1728-2810/article/view/622965
- DOI: https://doi.org/10.17816/socm622965
- ID: 622965
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Abstract
The capabilities of artificial intelligence (AI) and machine learning (ML) are growing at an unprecedented pace. These technologies have many useful applications, from machine translation to medical image analysis. Countless more such applications are currently being developed and can be expected in the long term. Unfortunately, not much attention has been paid to the weak and unpleasant sides of artificial intelligence. In our reviews, we examine the landscape of existing and potential problems associated with the use of innovative neural network technologies, suggesting that special attention be paid to ways to prevent and mitigate dangers and threats. The goal of our publication is to expand the circle of stakeholders and subject matter experts participating in the discussion of pressing issues of cyber security of medical AI, responsible approach to the vulnerabilities of neural network platforms, protection of equipment along with the formation of a safe landscape for its use, and the importance of legal and ethical regulatory tools.
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About the authors
Виталий Анатольевич Бердутин
ФБУЗ ПОМЦ ФМБА России
Author for correspondence.
Email: vberdt@gmail.com
кандидат медицинских наук, доцент кафедры выездного обучения по интегрированным дисциплинам
Russian FederationReferences
- Reshetnikov A.V., Shamshurina N.G., Shamshurin V.I. Economics and management in healthcare: Textbook and workshop. 2nd ed., trans. and additional M.: Yurayt Publishing House. 2020; 328. ISBN 978-5-534-10359-5 (in Russian).
- Reshetnikov A., Fedorova J., Prisyazhnaya N., Kolesnichenko O., Sotnik A., Shamshurina N. Health management for sustainable development. Proceedings of the 2nd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2018. 2. 2019; 51-56.
- Berdutin V. Socionic vision on Bioethics and Deontology. Lap Lambert Academic Publishing. 2018; Pg. no: 396. https://www.lap-publishing.com/catalog/details/store/ru/book/978-613-4-97887-3/socionic-vision-on-bioethics-and-deontology
- Liu J. Artificial Intelligence and Data Analytics Applications in Healthcare General. Review and Case Studies. CAIH2020: Proceedings of the 2020 Conference on Artificial Intelligence and Healthcare, October 2020, 49-53. https://dl.acm.org/doi/10.1145/3433996.3434006
- Daley K. Two arguments against human-friendly AI // AI and Ethics, 2021, 1(4), 435- 444. https://doi.org/10.1007/s43681-021-00051-6
- Vellido A. Societal Issues Concerning the Application of Artificial Intelligence in Medicine. Kidney Dis, 2019, 5(1), 11-17. https://doi.org/10.1159/000492428
- Breede E., Bayor E., Hersh F., et al. A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. Honolulu, HI, USA. CHI. 2020; paper 589; 1-12.
- Daneshjou R., Vodrahalli K., Novoa R.A., et al. Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set. Cornell University. 2022; 1-13. https://arxiv.org/ftp/arxiv/papers/2203/2203.08807.pdf
- Freeman K., Geppert J., Stinton Ch., Todkill D., et al. Use of artificial intelligence for image analysis in breast cancer screening programs: systematic review of test accuracy. BMJ. 2021; 374: n1872. doi: 10.1136/bmj.n 1872
- Wong A., Otles E., Donnelly J.P., Krumm A., et al. External Validation of a Widely Implemented Sepsis Prediction Model in Hospitalized Patients. JAMA Intern Med. 2021;181(8):1065-1070. doi: 10.1001/jamainternmed.2021.2626
- O’Leary L. How IBM’s Watson Went From the Future of Health Care to Sold Off for Parts. Slate, 2022. https://slate.com/technology/2022/01/ibm-watson-health-failure-artificial-intelligence.html#:~:text=Recently%2C%20Watson%20Health%20was%2C%20essentially,was%20more%20than%20%241%20billion
- Khan B., Hajira F., Qureshi A., Kumar S., Hanan A., Hussain J., Abdullah S. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomedical Materials & Devices. 2023; doi: 10.1007/s44174-023-00063-2
- Lee T.T., Kesselheim A.S. US food and drug administration precertification pilot program for digital health software: weighing the benefits and risks. Ann. Intern. Med. 2018. 168; 730-732.
- Parikh R.B., Teeple S., Navathe A.S. Addressing bias in artificial intelligence in health care. JAMA. 2019; 322 (24): 2377-2378.
- Challen R., Denny J., Pitt M., et al. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019; 28 (3): 231-237.
- He J., Baxter S.L., Xu J., Xu J., Zhou X., Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019; 25, 30-36.
- Monitoring the implementation of digital health: an overview of selected national and international methodologies. 2022. 70 р. https://www.who.int/europe/publications/i/item/WHO-EURO-2022-5985-45750-65816
- https://klasresearch.com/report/healthcare-ai-2019/1657
- https://cdn-aem.optum.com/content/dam/optum3/optum/
- https://www.fiercehealthcare.com/tech/investors-poured-4b-into-healthcare-ai-startups-2019
- https://www.aha.org/system/files/media/file/2019/11/Market_Insights_AI_Care_Delivery.pdf.
- https://www.technologyreview.com/hub/ai-effect/
- Avuçlu E. Determining the most accurate machine learning algorithms for medical diagnosis using the monk’ problems database and statistical measurements. Journal of Experimental & Theoretical Artificial Intelligence. 2023. https://doi.org/10.1080/0952813X.2023.2196984
- Shukla S. Enhancing Healthcare Insights, Exploring Diverse Use-Cases with K-means Clustering. International Journal of Management, IT & Engineering. 2023; 13 (8), 60-68.
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