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Session Preview
Webinar- Career in AI and ML
Module 1: Introduction to AI in Healthcare
What is Artificial Intelligence? Definitions and concepts
Historical background of AI in medicine
Categories: Machine Learning, NLP, Robotics, and Expert Systems
Overview of AI applications in healthcare domains
Opportunities and limitations
Class 1. AI in Health Care
Module 2: Fundamentals of Machine Learning
Supervised, Unsupervised, and Reinforcement Learning
Algorithms: Decision Trees, SVM, k-NN, Neural Networks
Model training, testing, and validation
Introduction to Deep Learning and Neural Networks
Relevance of ML in diagnostics and predictions
Class 2. AI in Health Care
Module 3: Data in Healthcare AI
Importance of healthcare data in AI systems
Types of healthcare data: structured and unstructured
Data collection sources: EMR, IoMT, clinical trials
Data quality, preprocessing, and annotation
Introduction to data privacy and compliance (HIPAA, GDPR)
Class 3. AI in Health Care
Module 4: Natural Language Processing (NLP) in Healthcare
Basics of NLP and its importance in clinical documentation
Applications: Chatbots, transcription, report generation
Named Entity Recognition, sentiment analysis, text classification
NLP challenges with medical terminology
Tools and platforms for medical NLP
Class 4. AI in Health Care
Module 5: AI in Medical Imaging and Diagnostics
Role of AI in radiology, pathology, and dermatology
Deep learning techniques in image classification
Case studies: Tumor detection, diabetic retinopathy, chest X-rays
Computer-Aided Diagnosis (CAD) systems
Challenges: Accuracy, bias, and clinical validation
Class 5. AI in Health Care
Module 6: AI in Drug Discovery and Development
AI's role in target identification and compound screening
Predictive models for drug–drug interactions and side effects
Case studies of AI in pharmaceutical R&D
AI in clinical trial design and patient recruitment
Accelerating timelines and reducing costs (without overpromising)
Class 6. AI in Health Care
Module 7: Robotics and AI-assisted Surgery
Surgical robotics: autonomy vs. assistance
AI in preoperative planning and intraoperative decision-making
Examples: Da Vinci surgical system, orthopedic robotics
Robotics in rehabilitation and elder care
Future scope and current limitations
Class 7. AI in Health Care
Module 8: AI in Hospital Operations and Patient Management
AI in scheduling, triage, and workflow automation
Virtual assistants and chatbots for patient engagement
AI in remote monitoring and personalized alerts
Bed occupancy prediction and staff allocation
Reducing administrative burden with AI tools
Class 8. AI in Health Care
Module 9: Ethical, Legal, and Social Implications (ELSI)
Data privacy, security, and informed consent
Algorithmic bias and fairness in healthcare AI
Explainability and trust in AI-driven decisions
Regulatory frameworks: FDA, EMA, CDSCO perspectives
Addressing the digital divide in healthcare delivery
Class 9. AI in Health Care
Module 10: Future Trends & Challenges in AI for Healthcare
Emerging technologies: Digital twins, IoMT, federated learning
AI integration with wearable devices and remote health tools
Interdisciplinary collaboration and skills for future healthcare
Global adoption trends and regional case studies
Final reflection on sustainability, ethics, and innovation
Class 10. AI in Health Care
Module 11 : Additional Session
Class 11. AI in Health Care
Module 12 : Upgrade Session
Class 12. AI in Health Care
AI In Healthcare Quiz Test