AI-assisted fibrosis scoring in MASH: Exploring pathologist decision-making with an SHG-based AI digital pathology tool

AbstractBackground Intra- and inter-reader variability complicates liver biopsy scoring for MASH. AI-powered digital pathology (AI-DP) is emerging as a pathologist assistant tool, with the promise of improving pathologist concordance. This study evaluates how Second Harmonic Generation (SHG)-based AI-DP influences pathologist decision-making for fibrosis scoring and offers guidance for integrating the SHG-based AI-assistance into pathology workflows. Methods Four pathologists reviewed 120 MASH cases with and without the assistance tool. We assessed changes in concordance, scoring, and pathologist perceptions via surveys and detailed case reviews. A qualitative analysis was performed on 22 selected cases (18% of the total number of MASH cases reviewed), comprising those with reduced or increased variability and discrepancies between pathologists and AI score. Results Pathologists reported the assistance tool as significantly helpful in 57% of cases, prompted conscious score changes in a further 12%, and not useful in 30%. Notably, score shifts between cases with and without assistance occurred even when the assistance tool was deemed unhelpful, indicating subtle influence. The assistance tool reduced discrepancies in 65% (33/51) of previously discrepant cases, but increased discrepancies in 27% (15/66 with 1-score discrepancy and 3/66 with more than 1-score discrepancy) of cases initially without discrepancies. This reflects inherent pathologist variability and differing thresholds and confidence in challenging or borderline cases. Evaluating different scenarios of concordance changes when using the assistance tool provided insights into pathologists' fibrosis staging considerations. Conclusion SHG-based AI-DP tools are promising adjuncts for fibrosis scoring in MASH. Our findings provide practical insights and recommendations for effective AI integration and highlight areas for future AI development. Collaborative human-AI approaches will be key to advancing precision histopathology and improving clinical trial outcomes in MASH. Impact and Implications AI DP as a pathologist's assistance tool can be utilized to improve pathologist concordance. However, pathologist judgement remains critical. In this study, we evaluated how pathologists used the assistance tool and how the platform impacted their decision-making. The findings are significant for pathologists and clinical researchers as they provide guidance for the well-informed use of SHG-based AI-DP as a tool to aid pathologists in fibrosis scoring in MASH. The insights gained can further inform policymakers about the integration of AI technologies in clinical trials or pathology workflow in clinical practice, promoting standardized practices.