Summary of Using Llm For Real-time Transcription and Summarization Of Doctor-patient Interactions Into Epuskesmas in Indonesia, by Azmul Asmar Irfan et al.
Using LLM for Real-Time Transcription and Summarization of Doctor-Patient Interactions into ePuskesmas in Indonesia
by Azmul Asmar Irfan, Nur Ahmad Khatim, Mansur M. Arief
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary of the abstract is as follows: The paper addresses inefficiencies in doctor-patient interactions at Puskesmas due to time-consuming consultations. Doctors spend considerable time diagnosing conditions, providing treatment advice, and transcribing notes into medical records. In linguistically diverse regions, doctors must ask clarifying questions, prolonging the process. To improve efficiency, this study proposes an AI-based solution using a localized large language model (LLM) for transcription, translation, and summarization. The Whisper model is utilized for transcription, while GPT-3 summarizes conversations into ePuskemas medical records format. This system is integrated as a web browser extension, enabling doctors to fill out patient forms during consultations. Real-time transcription, translation, and summarization enhance the quality of medical records and reduce the turnaround time for patient care. This innovation addresses challenges faced by healthcare providers in Indonesia, such as overcrowded facilities and administrative burdens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary is: The paper talks about how doctors spend a lot of time talking to patients, writing down notes, and figuring out what’s wrong. It takes even longer when the doctor doesn’t speak the patient’s language. To make things more efficient, this study suggests using artificial intelligence to help with tasks like typing up conversations and summarizing important information. This could save doctors time and help them focus on providing better care. |
Keywords
» Artificial intelligence » Gpt » Large language model » Summarization » Translation
