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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Generating Outpatient Progress Notes: A Comparison of Individualized and Generalized Models.

Jinsig Yoo^1,2, Hyeryun Park^1,2, Somin Song³

¹Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

An AI Doctor Assistant (DA) helps reduce medical documentation burdens by drafting clinical notes. A general AI model showed varied performance across tasks, highlighting the need for task-specific optimization in AI healthcare solutions.

Keywords:

AI Doctor Assistant Clinical Notes Fine-Tuning Generative Language Models Medical Documentation

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare

Background:

Increasing documentation workload in medical practice necessitates AI-driven solutions.
Clinical notes, particularly outpatient progress notes, represent a significant documentation burden.

Purpose of the Study:

To introduce an AI Doctor Assistant (DA) for generating drafts of outpatient progress notes.
To evaluate the performance of a general AI model versus individually fine-tuned models across different tasks.

Main Methods:

The AI DA was trained on datasets from four medical specialties.
A general model was trained on data from all professors and compared with individually fine-tuned models.
Performance was evaluated on two key tasks: generating the operation summary and clinical findings sections.

Main Results:

The general AI model's performance varied depending on the specific task.
Individual models fine-tuned for each professor showed task-dependent performance.
The study compared the efficacy of general versus specialized AI models for clinical note generation.

Conclusions:

The AI Doctor Assistant demonstrates potential in reducing clinical documentation burdens.
The generalized ability of AI models varies across different clinical tasks and specialties.
Findings suggest a need for careful consideration of model training strategies for AI in healthcare.