Open-Source Artificial Intelligence in medical applications

Abstract

Free and Open-Source Software (FOSS) and open-source artificial intelligence (AI) models have emerged as transformative paradigms in the educational and medical fields, promoting innovation, transparency, and decentralized collaboration. This study analyzes their impact through a systematic literature review and the examination of emblematic case studies, such as the City of Hope National Medical Center, Zauron Labs, Gene Outlook, and Cardiomentor. In the educational realm, FOSS has facilitated the creation of adaptive and personalized learning environments, reducing operational costs and fostering active participation from students and educators. Platforms like Moodle and Sakai have revolutionized online course management, while initiatives such as MIT OpenCourseWare have expanded access to high-quality educational resources, promoting a culture of transparency and collaboration.

In the medical field, open-source AI models have demonstrated their potential to improve diagnostic accuracy and personalized care. For example, the sepsis prediction model developed by City of Hope enables continuous monitoring of immunocompromised patients, while Zauron Labs' Guardian AI reduces errors in medical imaging interpretation. These applications stand out for their flexibility and ability to adapt to specific needs, making them valuable tools for clinical practice and research.