Artificial intelligence is rapidly transforming molecular biology and diagnostic laboratories, assisting with literature reviews, variant interpretation, primer design, protocol development, data analysis, and report generation. While these tools can significantly improve efficiency, they can also generate confident but incorrect answers that may go unnoticed in high-stakes clinical settings.

In The Hidden Risks of AI in Molecular Diagnostics, Dr. Rajesh R. Kundapur examines where AI succeeds, where it fails, and why scientific validation remains essential when human health and clinical decisions are involved. Drawing on extensive experience in molecular diagnostics, genomics, and laboratory management, the author presents a practical framework for evaluating and validating AI-generated outputs using the principles of the scientific method.

The book features 16 chapters which includes 21 real-world case studies demonstrating how AI can produce inaccurate interpretations, fabricated references, flawed experimental designs, incorrect calculations, misleading biological conclusions, and overconfident recommendations. Each case study illustrates how these errors can be detected, investigated, and corrected through systematic scientific validation.

Readers will learn how to identify AI hallucinations, assess confidence, implement verification workflows, establish quality-control checkpoints, and safely integrate AI into laboratory operations without compromising scientific rigor.

Whether you are a molecular biologist, clinical laboratory professional, bioinformatician, researcher, educator, student, or healthcare innovator, this book provides practical guidance for using AI responsibly in molecular diagnostics.

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