Role of foundation models in data-driven tissue diagnostics.

Mohsin Bilal¹, Aadam², Manahil Raza³, Anas Alsuhaibani⁴, Youssef N Altherwy⁴, Abdulrahman Alabduljabbar⁴, Fahdah Almarshad⁴, Paul Golding⁵, Nasir Rajpoot⁶

¹Information Systems Department, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia. Electronic address: m.farooq@psau.edu.sa.

²Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, United States of America.

³Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom.

⁴Information Systems Department, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.

⁵Frontier AI, San Francisco Bay Area, United States of America.

⁶Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom; Histofy Ltd, The Venture Centre, University of Warwick Science Park, Coventry, United Kingdom. Electronic address: N.M.Rajpoot@warwick.ac.uk.

PubMed DOI

Abstract

Artificial intelligence (AI)-based tissue diagnostics is entering a new phase driven by pathology foundation models: large-scale encoders and multimodal systems pretrained with self-supervised and vision-language objectives. Yet the evidence base has expanded faster than the methods used to evaluate and interpret claimed capabilities. The central clinical question is no longer whether these models…

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