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Course Outline
Introduction to Deep Learning
- Impact on the Medical Industry
- Successes and Failures in Deep Learning in Various Industries
Understanding Deep Learning
- Artificial Intelligence and Machine Learning
- Basic Concepts of Deep Learning
- Applications for Deep Learning
- The role of Big Data in Deep Learning
Overview of Common Deep Learning Techniques
- Neural Networks
- Natural Language Processing
- Image Recognition
- Speech Recognition
- Sentiment Analysis
Applying Deep Learning Techniques to Issues in Medicine
- Exploring the Opportunities for Improvement in the Medical Field
- Examining the Applicability of Deep Learning Techniques to the Cited Issues
Exploring Deep Learning Case Studies for Medicine
- DeepVentricle Algorithm for Ventricular Segmentation in Cardiac MR by Arterys
- Skin Cancer Diagnosis Algorithm by Stanford
- Heart Failure Prediction Algorithm by Sutter Health and the Georgia Institute of Technology
- Radiology Scans Diagnoses Across All Modalities by Behold.AI
- Clinical Decision Support Technologies by Enlitic
- Personalized Medicine and Therapies by Deep Genomics
- Decoding Cancer with Freenome
- Detection of Diabetic Retinopathy by Google
- Chatbot for Prevention and Diagnosis of Disease by Babylon Health
Limitations of Deep Learning
Ethical Implications and Data Privacy Concerns in Deep Learning
Creating New Business Models Based on Deep Learning-Enabled Platforms and Ecosystems
Bringing it All Together
- Choosing Deep Learning Solutions that Fit Your Needs
- Strategies for Adoption of Deep Learning Technologies
Team Communication and Managerial Buy-In
- Conversations with Managers and Leaders
- Conversations with Engineers and Data Scientists
Summary and Conclusion
Requirements
- Experience in the medical industry
- No programming experience is required
14 Hours