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This paper investigates the utility of GPTs in interactive medical diagnosis through natural language processing (NLP). The model dynamically engages patients in diagnostic discourse, generating contextually relevant follow-up questions grounded in symptomatology, test results, and attempted remedies. Preliminary experimentation showcases the model’s capacity to craft pertinent inquiries, offering nuanced insights into health concerns. The study emphasizes the necessity for ongoing development, addressing ethical dimensions associated with AI-guided diagnoses. The integration of GPTs into medical diagnostics not only accentuates improved patient interactions but also signifies a potential paradigm shift in diagnostic support within the healthcare domain. This research lays the groundwork for future innovations in diagnostic methodologies, positioning NLP-driven interactions as a promising avenue for enhancing the effectiveness of medical diagnosis.

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Medical Diagnosis Through Conversational Intelligence Using Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • S. Akilesh,
  • R. Suganya,
  • Swarup Ravi,
  • R. Tulasi Raman,
  • Vignesh Srinivasakumar,
  • Girish H. Subramanian

摘要

This paper investigates the utility of GPTs in interactive medical diagnosis through natural language processing (NLP). The model dynamically engages patients in diagnostic discourse, generating contextually relevant follow-up questions grounded in symptomatology, test results, and attempted remedies. Preliminary experimentation showcases the model’s capacity to craft pertinent inquiries, offering nuanced insights into health concerns. The study emphasizes the necessity for ongoing development, addressing ethical dimensions associated with AI-guided diagnoses. The integration of GPTs into medical diagnostics not only accentuates improved patient interactions but also signifies a potential paradigm shift in diagnostic support within the healthcare domain. This research lays the groundwork for future innovations in diagnostic methodologies, positioning NLP-driven interactions as a promising avenue for enhancing the effectiveness of medical diagnosis.