Diagnosing AI: Evaluation of AI in Clinical Practice is a vital and timely guide for clinicians, data scientists, and decision-makers at the intersection of artificial intelligence and medicine. As large language models (LLMs) and AI systems become increasingly visible in healthcare, this book provides a clear, evidence-based framework for understanding their true capabilities and limitations in clinical deployment.

A Warning Against Misplaced Confidence
With the rise of AI chatbots that produce confident, fluent medical text, there is a real risk of mistaking eloquence for expertise. Dr. Milan Toma demonstrates, through empirical studies and systematic analysis, that LLMs can produce authoritative-sounding reports while masking fundamental errors and inconsistencies. The temptation to trust AI-generated prose as a substitute for rigorous clinical reasoning is not only misleading, but potentially dangerous.

A Practical Framework for Evaluation
Diagnosing AI goes beyond critique to offer a comprehensive, step-by-step framework for evaluating machine learning systems in medicine. You will learn to distinguish between models that have truly learned and those that merely imitate expertise, and how to translate clinical priorities into meaningful evaluation criteria.

What You Will Learn:
How LLMs create the illusion of expertise and why their confident tone should not be mistaken for diagnostic accuracy.
Empirical evidence showing that leading multimodal language models often make critical errors and disagree on basic findings, making them unsuitable for autonomous medical image interpretation.
The landscape of machine learning in medicine, from traditional algorithms to modern transformers, and which approaches are appropriate for which diagnostic tasks.
Real-world applications of proven machine learning algorithms in cardiovascular and other specialties, and the importance of validation and regulatory clearance.
The challenge of class imbalance in medical data, where rare diseases can be missed by models focused on overall accuracy, and strategies to address this.
How to develop clinically oriented evaluation protocols that reflect real-world priorities, recognizing that not all errors are equal (for example, missing a disease vs. a false alarm).
Why learning dynamics and training-validation curves are more revealing than final accuracy metrics, and how to use them to judge model reliability and generalizability.
The limitations of language models in interpreting diagnostic plots, and why human expertise remains essential in evaluating model quality.
Economic factors in AI deployment, integrating operational costs, error consequences, and implementation expenses to determine whether a system is worth investing in.

For Clinicians, Data Scientists, and Healthcare Leaders
Throughout the book, Dr. Toma emphasizes a critical distinction: general-purpose language models may generate plausible medical text, but only specialized, validated machine learning systems are suitable for high-stakes clinical applications. For tasks like medical image interpretation, the gold standard remains purpose-built machine learning—not general chatbots, no matter how impressive their language skills.

By the end of Diagnosing AI, readers will have a robust framework for evaluating clinical machine learning systems, from initial training dynamics through cost-effectiveness. You will understand why learning curves matter more than final metrics, why class imbalance demands special treatment, and why confident AI prose provides no guarantee of accuracy. In medicine, where the stakes are measured in human welfare, such understanding is not simply helpful; it is essential.