TY - GEN
T1 - Medical Diagnosis Through Conversational Intelligence Using Large Language Models
AU - Akilesh, S.
AU - Suganya, R.
AU - Ravi, Swarup
AU - Tulasi Raman, R.
AU - Srinivasakumar, Vignesh
AU - Subramanian, Girish H.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105002729771
UR - https://www.scopus.com/inward/citedby.url?scp=105002729771&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-4711-5_23
DO - 10.1007/978-981-97-4711-5_23
M3 - Conference contribution
AN - SCOPUS:105002729771
SN - 9789819747108
T3 - Lecture Notes in Electrical Engineering
SP - 327
EP - 338
BT - 5th International Conference on Computing and Network Communications - Proceedings of CoCoNet 2023
A2 - Thampi, Sabu M.
A2 - Chaudhary, Vipin
A2 - Pathan, Al-Sakib Khan
A2 - Ching Li, Kuan
A2 - Krishnaswamy, Dilip
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Computing and Network Communications, CoCoNet 2023
Y2 - 18 December 2023 through 20 December 2023
ER -