Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Open-Source Technology

Background:

• Machine learning (ML) models are increasingly approved for clinical use, yet operate in immature regulatory and market environments.
• The digital culture often embraces open-source solutions for innovation sharing, contrasting with healthcare's proprietary data infrastructure.

Purpose of the Study:

• To discuss the feasibility and implications of implementing open-source machine learning within the existing proprietary healthcare information infrastructure.
• To explore the advantages and significant challenges associated with open-source ML in healthcare.

Main Methods:

• Literature review and discussion of current trends in machine learning adoption in healthcare.
• Analysis of the interplay between open-source principles and proprietary data environments.
• Examination of regulatory, economic, and technical factors influencing open-source ML deployment.

Main Results:

• Open-source ML offers potential benefits such as reduced development costs, enhanced product integrity, customizability, and increased accessibility.
• Significant hurdles include engineering concerns (infrastructure, safety), lack of intellectual property incentives, and unclear liability frameworks.

Conclusions:

• Reconciling open-source ML with proprietary healthcare data requires active policy, regulatory, and organizational efforts.
• A conducive market must be crafted to encourage continued innovation and collaboration among developers in open-source healthcare ML.