Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

AI as a Medical Ally: Evaluating ChatGPT's Usage and Impact in Indian Healthcare

Aryaman Raina, Prateek Mishra, Harshit goyal, Dhruv Kumar

TL;DR

This study examines how LLMs like ChatGPT are used in Indian healthcare by both general users and medical professionals. Using a mixed-methods approach with 46 surveys and 6 in-depth interviews, it maps usage patterns, benefits, and trust-related concerns, highlighting education, preliminary diagnostics, and patient communication as key applications. The findings reveal strong enthusiasm for AI-assisted information access but significant worries about accuracy, source credibility, and privacy, underscoring the need for human oversight and regulatory safeguards. The paper contributes practical insights for developers and policymakers to ensure ethically compliant, context-specific, and mutually reinforcing integration of AI tools in healthcare. It also points to future research directions and the importance of tailoring LLMs to diverse Indian healthcare contexts.

Abstract

This study investigates the integration and impact of Large Language Models (LLMs), like ChatGPT, in India's healthcare sector. Our research employs a dual approach, engaging both general users and medical professionals through surveys and interviews respectively. Our findings reveal that healthcare professionals value ChatGPT in medical education and preliminary clinical settings, but exercise caution due to concerns about reliability, privacy, and the need for cross-verification with medical references. General users show a preference for AI interactions in healthcare, but concerns regarding accuracy and trust persist. The study underscores the need for these technologies to complement, not replace, human medical expertise, highlighting the importance of developing LLMs in collaboration with healthcare providers. This paper enhances the understanding of LLMs in healthcare, detailing current usage, user trust, and improvement areas. Our insights inform future research and development, underscoring the need for ethically compliant, user-focused LLM advancements that address healthcare-specific challenges.

AI as a Medical Ally: Evaluating ChatGPT's Usage and Impact in Indian Healthcare

TL;DR

This study examines how LLMs like ChatGPT are used in Indian healthcare by both general users and medical professionals. Using a mixed-methods approach with 46 surveys and 6 in-depth interviews, it maps usage patterns, benefits, and trust-related concerns, highlighting education, preliminary diagnostics, and patient communication as key applications. The findings reveal strong enthusiasm for AI-assisted information access but significant worries about accuracy, source credibility, and privacy, underscoring the need for human oversight and regulatory safeguards. The paper contributes practical insights for developers and policymakers to ensure ethically compliant, context-specific, and mutually reinforcing integration of AI tools in healthcare. It also points to future research directions and the importance of tailoring LLMs to diverse Indian healthcare contexts.

Abstract

This study investigates the integration and impact of Large Language Models (LLMs), like ChatGPT, in India's healthcare sector. Our research employs a dual approach, engaging both general users and medical professionals through surveys and interviews respectively. Our findings reveal that healthcare professionals value ChatGPT in medical education and preliminary clinical settings, but exercise caution due to concerns about reliability, privacy, and the need for cross-verification with medical references. General users show a preference for AI interactions in healthcare, but concerns regarding accuracy and trust persist. The study underscores the need for these technologies to complement, not replace, human medical expertise, highlighting the importance of developing LLMs in collaboration with healthcare providers. This paper enhances the understanding of LLMs in healthcare, detailing current usage, user trust, and improvement areas. Our insights inform future research and development, underscoring the need for ethically compliant, user-focused LLM advancements that address healthcare-specific challenges.
Paper Structure (16 sections, 2 figures, 5 tables)

This paper contains 16 sections, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Research Design
  5. Data Collection and Analysis
  6. Limitations
  7. Quantitative Evaluation
  8. Qualitative Evaluation
  9. Usage Patterns and Benefits of ChatGPT
  10. Challenges with the use of ChatGPT
  11. Perceptions and Recommendations
  12. Discussion
  13. Conclusion
  14. Survey Questionnaire
  15. Interview Questionnaire
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Data visualizations for the survey responses (Part 1)
  • Figure 2: Data visualizations for the survey responses (Part 2)