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Ways to improve the efficiency of implementing artificial intelligence systems in medical practice
- Authors: Shmonova M.A.1, Avacheva T.G.1
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Affiliations:
- Ryazan State Medical University named after academician I.P. Pavlov
- Issue: Vol 4, No 1S (2023)
- Pages: 148-150
- Section: Conference proceedings
- URL: https://jdigitaldiagnostics.com/DD/article/view/430376
- DOI: https://doi.org/10.17816/DD430376
- ID: 430376
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Abstract
BACKGROUND: The medical market offers interesting solutions that use artificial intelligence (AI) technologies; however, such solutions often remain at the startup level or are used locally. The question is how to achieve the maximum when introducing AI systems into medical practice.
AIM: To answer the question of why the large number of existing developments in AI and medical decision support systems are not used as widely as medical information systems, telemedical consultations, and other health IT solutions. Possible ways to improve the efficiency of implementing AI technologies and medical decision support systems in the work of physicians were presented.
METHODS: Theoretical and general scientific (analysis of literature and Internet sources on the problem of research, synthesis, generalization, comparison, and systematization) and empirical (observation, interview, and testing) methods were used.
RESUTLS: The main barriers to the effective implementation of AI systems in medical practice and possible options to solve the following problems were highlighted.
- Problem 1: incorrect data collection. Solution: care must be taken with the accumulation of materials used for the analysis and training by AI systems in medicine.
- Problem 2: incompetent developers. Solutions: involvement of effective third-party specialists or training one’s own.
- Problem 3: medical workers’ and/or patients’ aversion to AI technologies. Solutions: education in the successful application of AI technologies and medical decision support systems in healthcare and involvement of practicing physicians as experts during the AI conceptualization.
CONCLUSIONS: If the above criteria for the development and use of AI technologies and medical decision support systems are met, the effect of their introduction into medical practice will tend to be maximized.
Full Text
BACKGROUND: The medical market offers interesting solutions that use artificial intelligence (AI) technologies; however, such solutions often remain at the startup level or are used locally. The question is how to achieve the maximum when introducing AI systems into medical practice.
AIM: To answer the question of why the large number of existing developments in AI and medical decision support systems are not used as widely as medical information systems, telemedical consultations, and other health IT solutions. Possible ways to improve the efficiency of implementing AI technologies and medical decision support systems in the work of physicians were presented.
METHODS: Theoretical and general scientific (analysis of literature and Internet sources on the problem of research, synthesis, generalization, comparison, and systematization) and empirical (observation, interview, and testing) methods were used.
RESUTLS: The main barriers to the effective implementation of AI systems in medical practice and possible options to solve the following problems were highlighted.
- Problem 1: incorrect data collection. Solution: care must be taken with the accumulation of materials used for the analysis and training by AI systems in medicine.
- Problem 2: incompetent developers. Solutions: involvement of effective third-party specialists or training one’s own.
- Problem 3: medical workers’ and/or patients’ aversion to AI technologies. Solutions: education in the successful application of AI technologies and medical decision support systems in healthcare and involvement of practicing physicians as experts during the AI conceptualization.
CONCLUSIONS: If the above criteria for the development and use of AI technologies and medical decision support systems are met, the effect of their introduction into medical practice will tend to be maximized.
About the authors
Marina A. Shmonova
Ryazan State Medical University named after academician I.P. Pavlov
Author for correspondence.
Email: shmon-marina@yandex.ru
ORCID iD: 0000-0001-9308-8766
Russian Federation, Ryazan
Tatyana G. Avacheva
Ryazan State Medical University named after academician I.P. Pavlov
Email: Mfmi.rzgmu@mail.ru
ORCID iD: 0000-0002-2099-655X
Russian Federation, Ryazan
References
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