New Book Published

Diagnosing AI: Evaluation of AI in Clinical Practice

by Milan Toma

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. This book demonstrates, through empirical studies and systematic analysis, that LLMs can produce authoritative-sounding reports while masking fundamental errors and inconsistencies.

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 critical errors in multimodal language models for medical image interpretation
• The landscape of machine learning in medicine, from traditional algorithms to modern transformers
• Real-world applications of proven ML algorithms in cardiovascular and other specialties
• How to develop clinically oriented evaluation protocols that reflect real-world priorities
• Why learning dynamics and training-validation curves are more revealing than final accuracy metrics
• Economic factors in AI deployment, integrating operational costs and error consequences