Medical doctor's perception of artificial intelligence during the COVID-19 era: A mixed methods study

• Published in: Journal of family medicine and primary care Vol. 13; no. 5; pp. 1931 - 1936
• Main Authors: Dongre, Ashwini S, More, Sandeep D, Wilson, Vidhya, Singh, R Jai
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer Medknow Publications 01-05-2024
• Subjects: acceptance; artificial intelligence; covid-19; medical data analysts; medical doctor; COVID-19; Acceptance; medical doctor; artificial intelligence; medical data analysts
• ISSN: 2249-4863; 2278-7135
• DOI: 10.4103/jfmpc.jfmpc_1543_23

Artificial intelligence (AI) has led to the development of various opportunities during the COVID-19 pandemic. An abundant number of applications have surfaced responding to the pandemic, while some other applications were futile.
The present study aimed to assess the perception and opportunities of AI used during the COVID-19 pandemic and to explore the perception of medical data analysts about the inclusion of AI in medical education.
This study adopted a mixed-method research design conducted among medical doctors for the quantitative part while including medical data analysts for the qualitative interview.
The study reveals that nearly 64.8% of professionals were working in high COVID-19 patient-load settings and had significantly more acceptance of AI tools compared to others (
< 0.05). The learning barrier like engaging in new skills and working under a non-medical hierarchy led to dissatisfaction among medical data analysts. There was widespread recognition of their work after the COVID-19 pandemic.
Notwithstanding that the majority of professionals are aware that public health emergency creates a significant strain on doctors, the majority still have to work in extremely high case load setting to demand solutions. AI applications are still not being integrated into medicine as fast as technology has been advancing. Sensitization workshops can be conducted among specialists to develop interest which will encourage them to identify problem statements in their fields, and along with AI experts, they can create AI-enabled algorithms to address the problems. A lack of educational opportunities about AI in formal medical curriculum was identified.