Beware of High Accuracy Claims: A Call for Responsible Medical AI Evaluation скачать в хорошем качестве

Beware of High Accuracy Claims: A Call for Responsible Medical AI Evaluation

A 99% accurate AI system that misses every single case of disease is not a success; it's a failure. In this presentation, Dr. Milan Toma exposes the accuracy paradox in medical AI and issues a call to action for clinicians, developers, and patients. Learn why impressive accuracy numbers can mask clinically useless systems, how imbalanced medical data creates misleading metrics, and what evaluation standards we should demand before trusting AI with patient care. Topics covered: The accuracy paradox: when 99% accuracy means 0% disease detection. Why medical data is inherently imbalanced. Real-world consequences of AI systems that miss diagnoses. The asymmetry of medical errors: false negatives vs. false positives. Better evaluation metrics: Sensitivity, Specificity, F1 Score, MCC, Balanced Accuracy. Key takeaways for rigorous medical AI evaluation. Calls to action for clinicians, developers, and patients. For more detailed guidance on medical AI evaluation: 📚 Diagnosing AI by Dr. Milan Toma (2026) https://www.amazon.com/dp/B0GQGHL3G3 📚 AI-Assisted Medical Diagnostics by Dr. Milan Toma (2025) https://www.amazon.com/dp/B0FR9K35WY Presented by: Dr. Milan Toma, PhD, SMIEEE Associate Professor of Clinical Sciences College of Osteopathic Medicine New York Institute of Technology #MedicalAI #AccuracyParadox #AIEvaluation #HealthcareAI #ResponsibleAI #PatientSafety #ClinicalAI #MachineLearning