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

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Published on: July 11, 2025

Advances and Trends in Clinical Information Systems: From AI to Machine Learning Applications and Beyond.

Elizabeth M Borycki¹, Hrishikesh S Nair¹, Andre W Kushniruk¹

¹School of Health Information Science, University of Victoria, Canada.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

Clinical Information Systems (CIS) are evolving with Artificial Intelligence (AI) and Machine Learning (ML) integration. This integration enhances healthcare decision-making, efficiency, and equity, transforming patient care.

Keywords:

Clinical information systems artificial intelligence electronic health records human factors interoperability machine learning patient centred safety usability

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Last Updated: May 24, 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:

Healthcare Informatics
Medical Artificial Intelligence
Health Systems Research

Background:

Clinical Information Systems (CIS) are foundational to modern healthcare, supporting data-driven decisions and patient-centered care.
The integration of advanced technologies like AI and ML is transforming CIS capabilities.
Understanding the global research landscape of CIS, particularly AI/ML integration, is crucial for future development.

Purpose of the Study:

To conduct a scoping review on the global research of Clinical Information Systems (CIS).
To specifically investigate the integration of Artificial Intelligence (AI) and Machine Learning (ML) within CIS.
To identify trends and key areas of study in CIS research from 2021-2025.

Main Methods:

A scoping review methodology was employed.
Data were extracted from 31 peer-reviewed articles published between 2021 and 2025.
Literature searches were conducted using PubMed® and Web of Science® databases.

Main Results:

CIS are transitioning from static data repositories to dynamic platforms.
Key advancements include intelligent decision support, cross-system interoperability, and enhanced patient-centered features.
AI and ML integration is a significant trend in current CIS research.

Conclusions:

The integration of AI and ML into interoperable CIS frameworks holds substantial potential.
These advancements can significantly improve diagnostic accuracy and operational efficiency in healthcare.
Implementing AI/ML in CIS can advance health equity by optimizing resource allocation and access to care.