Explore the potential of deep learning in revolutionizing medical diagnoses through this Stanford seminar. Delve into the traditional clinical diagnostic procedure and its evolution with AI integration. Examine information gathering steps and diagnostic testing enhanced by machine learning frameworks. Discover groundbreaking applications in arrhythmia detection, continuous monitoring advancements, and automated detection challenges. Investigate the use of residual networks and wide ResNets in addressing medical errors. Learn about chest radiograph interpretation, focusing on detecting abnormalities and pneumonia using DenseNets. Analyze the limitations and interpretability of AI in medical imaging through class activation maps. Gain insights into the future of diagnostic access and clinical decision support, with a focus on knee MRI interpretation. Evaluate the importance of external validation and contemplate the transformative impact of AI on the future of medical diagnostics.
Overview
Syllabus
Introduction.
Traditional Model of Clinical.
Diagnostic Procedure with Al.
Information Gathering Step.
Diagnostic Testing with Al.
Machine Learning Framework.
Next Paradigm shift?.
Arrhythmia detection.
Future of continuous monitoring.
Holter Monitor.
Amount of data capture.
Automated Detection Challenges.
Previous Approaches.
Setup.
Network Architecture.
Residual Networks.
Wide ResNets.
Dataset - Test Set.
Medical Errors.
Chest Radiograph Interpretation.
Chest X-ray exam.
Detecting Abnormalities.
X-ray findings of pneumonia.
Detecting Pneumonia.
DenseNets.
Dataset - Train Set.
Evaluation -- Limitations.
Class Activation Maps.
Interpretations.
Future of diagnostic access.
Diagnosis Clinical Decision Support.
Knee MR.
Input.
Dataset Training on.
Interpretability.
External Validation.
Diagnostic Future with Al.
Taught by
Stanford Online
