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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Artificial intelligence (AI)-aided clinical data management: Applications, human-in-the-loop workflows, and

Saya Ohi¹, Tomoko Iwamoto², Daiki Ikeda¹

¹Office of Bioinformatics, Department of Joint Center for Researchers, Associates and Clinicians (JCRAC), Center for Clinical Sciences, Japan Institute for Health Security, Tokyo, Japan.

Global Health & Medicine

|June 29, 2026

Summary

Artificial intelligence (AI) can support clinical data management (CDM) in Japan, addressing personnel shortages and data complexity. A human-in-the-loop approach, combining AI processing with human oversight, is key for maintaining data quality in academic research.

Keywords:

AI-related regulations and guidelines artificial intelligence (AI)clinical data management (CDM)human-in-the-loop (HITL)

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Last Updated: Jun 30, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Clinical Research
Data Management
Artificial Intelligence

Background:

Clinical data management (CDM) is crucial for research quality.
Japan faces challenges in CDM, including personnel shortages and increasing data complexity, especially in academic research organizations (AROs).
Advancements in AI, particularly large language models, offer potential solutions for supporting CDM tasks.

Purpose of the Study:

To review domestic and international examples of AI applications in CDM.
To identify common implementation principles for AI in CDM.
To discuss the implications of AI in CDM for regulatory frameworks and risk management.

Main Methods:

Literature review of AI utilization in CDM-related tasks.
Analysis of case studies on AI in data cleaning, medical coding, and query generation.
Examination of emerging regulatory guidelines and principles for AI in clinical research.

Main Results:

AI shows promise in supporting CDM tasks like data cleaning, medical coding, and query generation.
A consistent implementation principle is the 'human-in-the-loop' design, where AI assists human decision-making.
Regulatory frameworks are evolving, but integrating AI into Good Clinical Practice (GCP) requires further discussion.

Conclusions:

Human-AI collaborative workflows are accelerating the shift from manual CDM processes.
Robust technical, regulatory, and risk-management frameworks are essential for successful AI integration in CDM.
AI and data quality have a symbiotic relationship, with improvements in one enhancing the other.