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

Authors: Aryaman Raina, Prateek Mishra, Harshit goyal, Dhruv Kumar

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Results of the summarizing process for the arXiv paper: 2401.15605v1

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Authors Aryaman Raina, Prateek Mishra, Harshit Goyal, and Dhruv Kumar conducted a study titled "AI as a Medical Ally: Evaluating ChatGPT's Usage and Impact in Indian Healthcare" to explore the integration and impact of Large Language Models (LLMs) like ChatGPT in India's healthcare sector. The research utilized a dual approach, engaging both general users and medical professionals through surveys and interviews. The findings revealed that healthcare professionals value ChatGPT for its utility in medical education and preliminary clinical settings; however, they expressed concerns about reliability, privacy, and the necessity for cross-verification with medical references. On the other hand, general users displayed a preference for AI interactions in healthcare but remained cautious due to lingering doubts regarding accuracy and trustworthiness. The study emphasized the importance of LLMs complementing rather than replacing human medical expertise. It underscored the significance of collaborative development between technology providers and healthcare professionals to ensure that these tools meet industry-specific needs effectively. By enhancing the understanding of LLMs in healthcare, detailing current usage patterns, user trust levels, and areas for improvement,this research contributes valuable insights for future advancements in the field. The authors highlighted the need for ethically compliant and user-focused LLM advancements that address unique challenges within the healthcare domain. Overall,this study sheds light on the evolving role of AI technologies like ChatGPT in transforming healthcare practices in India while emphasizing the critical role of human expertise alongside technological innovations.
AI-generated keywords: AI in Healthcare Large Language Models ChatGPT Medical Education Collaborative Development

AI-generated Key Points

  • Authors conducted a study titled "AI as a Medical Ally: Evaluating ChatGPT's Usage and Impact in Indian Healthcare"
  • Research utilized surveys and interviews with general users and medical professionals
  • Healthcare professionals value ChatGPT for medical education and preliminary clinical settings but have concerns about reliability, privacy, and cross-verification
  • General users prefer AI interactions in healthcare but are cautious due to doubts about accuracy and trustworthiness
  • Emphasized importance of LLMs complementing human medical expertise rather than replacing it
  • Highlighted significance of collaborative development between technology providers and healthcare professionals
  • Research contributes insights on current LLM usage patterns, user trust levels, and areas for improvement in healthcare
  • Emphasized need for ethically compliant and user-focused LLM advancements in healthcare
  • Study sheds light on the evolving role of AI technologies like ChatGPT in transforming healthcare practices in India
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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.

Submitted to arXiv on 28 Jan. 2024

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