# ABC’S of AI: proposing a clinician’s framework for the interpretation of artificial intelligence studies

## TL;DR

This paper introduces a framework to help clinicians evaluate AI studies, using a case example from septic shock treatment.

## Contribution

A novel ABCDEFG framework for clinicians to assess AI studies' reliability and relevance.

## Key findings

- The framework highlights methodological strengths like reporting standards and external validation.
- Limitations include challenges in causal inference and reward structure design in AI models.
- The framework provides a roadmap for integrating AI into clinical decision-making responsibly.

## Abstract

Artificial intelligence (AI) and machine learning have emerged as transformative analytic methods capable of identifying patterns and proposing treatment strategies from complex, heterogeneous data generated in patient care settings. However, translating reports of AI tools into clinical decision-making requires careful interpretation and contextualization. Here, we present a structured framework (ABCDEFG framework) for clinicians to critically appraise studies reporting AI tools using a high-profile reinforcement learning analysis of vasopressin initiation in septic shock as a case example. We highlight key methodological strengths including adherence to reporting standards, alignment with clinical outcomes, and external validation. We also identify limitations related to causal inference, reward structure design, assumptions about state representation, and implementation readiness. Our framework emphasizes the importance of rigorous, clinically grounded critique before integrating AI tools into practice, and provides a roadmap for clinicians to assess the reliability and relevance of emerging AI-based decision support.