Practicing Medicine in the Age of Machines: Physician’s Perspectives on Artificial Intelligence in Chengalpattu – A Cross-Sectional Study
DOI:
https://doi.org/10.63682/jns.v14i6.4164Keywords:
Acceptance of AI, AI in healthcare, Artificial intelligence, Physician’s attitudeAbstract
Background: Artificial Intelligence (AI) is reshaping healthcare by enhancing diagnosis, treatment, and patient care. Its successful implementation hinges on physicians' confidence, acceptance, and perceived utility. Understanding these factors is vital to addressing barriers and facilitating integration into clinical practice.
Methodology: A cross-sectional study was conducted among 224 allopathic doctors from hospitals in Chengalpattu district. Participants were selected randomly, and data were collected using a pretested questionnaire with 11 closed-ended questions.
Results: Only 27 participants (12.1%) were familiar with AI. While AI's ability to expedite processes was acknowledged, 84 participants (37.5%) expressed concerns about its inability to empathize with patients. Few participants believed AI was diagnostically superior to doctors (9.8%) or capable of replacing them (17.9%). Gender and experience significantly influenced attitudes, with females and those with over 12 years of experience expressing more negative views. The field of work had minimal impact on attitudes.
Conclusion: The study highlights a significant gap in physicians' awareness and confidence in AI within Chengalpattu district. Targeted education and training are essential to bridge this gap and address concerns. Collaborating with physicians in AI development and integrating human expertise with AI will be critical for fostering trust and acceptance in clinical practice
Downloads
Metrics
References
Artificial Intelligence: A Modern Approach, 4th US ed. [Internet]. [cited 2024 Sep 27]. Available from: https://aima.cs.berkeley.edu/
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017 Dec;2(4):230–43.
Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. NPJ Digit Med. 2018;1:5.
4. Reddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019 Jan 1;112(1):22–8.
Telemedicine Society of India and Practo launch ‘Rise of Telemedicine - 2020’ report – Practo Digest [Internet]. [cited 2024 Sep 30]. Available from: https://blog.practo.com/telemedicine-society-of-india-and-practo-launch-rise-of-telemedicine-2020-report/
ndhm_strategy_overview.pdf [Internet]. [cited 2024 Sep 30]. Available from: https://www.niti.gov.in/sites/default/files/2021-09/ndhm_strategy_overview.pdf
Nawaz FA, Yaqoob S, Sharma A, Khan AR, Rackimuthu S, Ghazi BK, et al. From black to white: A roadmap to containing the rise of candidiasis amidst COVID-19 and mucormycosis in India. Clin Epidemiol Glob Health. 2021;12:100917.
Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016 Sep 29;375(13):1216–9.
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019 Jan;25(1):30–6.
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018 Oct;2(10):719–31.
2024050974.pdf [Internet]. [cited 2024 Dec 25]. Available from: https://cdn.s3waas.gov.in/s39778d5d219c5080b9a6a17bef029331c/uploads/2024/05/2024050974.pdf
Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH. Physician Confidence in Artificial Intelligence: An Online Mobile Survey. J Med Internet Res. 2019 Mar 25;21(3):e12422.
F C, R R, Gf G. Unintended Consequences of Machine Learning in Medicine. JAMA [Internet]. 2017 Aug 8 [cited 2024 Oct 13];318(6). Available from: https://pubmed.ncbi.nlm.nih.gov/28727867/
Ej T. High-performance medicine: the convergence of human and artificial intelligence. Nat Med [Internet]. 2019 Jan [cited 2024 Oct 13];25(1). Available from: https://pubmed.ncbi.nlm.nih.gov/30617339/
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017 Dec;2(4):230–43.
A E, A R, B R, V K, M D, K C, et al. A guide to deep learning in healthcare. Nat Med [Internet]. 2019 Jan [cited 2024 Oct 13];25(1). Available from: https://pubmed.ncbi.nlm.nih.gov/30617335/
Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019 Mar;24(3):773–80.
Regulation of predictive analytics in medicine | Science [Internet]. [cited 2024 Oct 13]. Available from: https://www.science.org/doi/10.1126/science.aaw0029
Castaneda C, Nalley K, Mannion C, Bhattacharyya P, Blake P, Pecora A, et al. Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. J Clin Bioinforma. 2015 Mar 26;5:4.
Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94–8.
Petrakaki D. . . : . . pp £15.99 (pbk) £50 (hbk) ISBN 978-1-5095-0059-8. Sociol Health Illn. 2017;39(8):1574–5.
Wang F, Preininger A. AI in Health: State of the Art, Challenges, and Future Directions. Yearb Med Inform. 2019 Aug;28(1):16–26.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
