Evaluation of Clinical AI-Based Diagnostic Solutions – A Multiperspective, Interdisciplinary Approach

Abstract

INTRODUCTION: The primary goal of developing new clinical diagnostic solutions is to create value for healthcare. The rapid rise of artificial intelligence (AI)-based diagnostics has led to a surge in publications and, to a lesser extent, market-ready tools. Clinicians must now integrate these innovations to manage increasing data volumes, making it challenging to assess the added value of new tools in the diagnostic workflow. METHODS: The INTERREG Baltic Sea Region project "Clinical Artificial Intelligence-Based Diagnostics (CAIDX)" developed a comprehensive blueprint guiding the process from identifying clinical needs to implementing certified AI products in diagnostics. The approach emphasizes systematic evaluation at each development stage and throughout the AI solution's lifecycle, incorporating diverse stakeholder perspectives and a range of evaluation methodologies. RESULTS: The CAIDX project produced the "Clinical AI-Pathway," an end-to-end framework for integrating AI-based diagnostic tools. This framework provides methodologies and tools for systematic evaluation at all stages, ensuring alignment with clinical needs and rigorous assessment of value. CONCLUSIONS: Systematic, multi-perspective evaluation is crucial for successfully integrating AI diagnostics into clinical practice. The "Clinical AI-Pathway" framework offers a structured method for assessing and implementing AI solutions, supporting their value-driven adoption in healthcare. The framework, available at ClinicalAI.eu, aims to facilitate broader and more effective use of AI in clinical diagnostics.

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