Artificial_Intelligence_in_Medicine.pptx

Artificial_Intelligence_in_Medicine.pptx

1. Artificial Intelligence in Medicine For MBBS Students

2. Introduction to Artificial Intelligence Definition: Simulation of human intelligence in machines. AI enables computers to think, learn, and make decisions. Key fields: Machine Learning, Deep Learning, NLP , Robotics.

3. History of AI 1950s: Concept introduced by Alan Turing. 1956: Term 'Artificial Intelligence' coined at Dartmouth Conference. 2000s: Rise of Machine Learning and Big Data. 2010s–2020s: AI revolutionize d industries including healthcare.

4. Basic Concepts in AI Machine Learning – systems learn from data. Deep Learning – neural networks mimicking human brain. Natural Language Processing – understanding human language. Computer Vision – interpreting medical images.

5. Why AI in Medicine? To improve diagnostic accuracy. To enhance treatment planning and decision-making. To reduce workload and human error. To support personalized medicine.

6. Applications in Diagnostics AI assists in detecting diseases using imaging (X-ray, MRI, CT). Helps identify early signs of cancer, stroke, and fractures. Improves diagnostic speed and accuracy.

7. AI in Radiology AI algorithms analyze scans for tumors and anomalies. Example: Detection of lung nodules, brain hemorrhage. Reduces reporting time and enhances precision.

8. AI in Pathology Digital pathology supported by AI for slide image analysis. Assists in cancer grading and detection. Improves reproducibility and reduces subjectivity.

9. AI in Surgery Robotic-assisted surgeries improve precision. AI predicts surgical risks and outcomes. Example: Da Vinci Surgical System.

10. AI in Ophthalmology AI detects diabetic retinopathy and glaucoma . Automated screening improves accessibility.

11. AI in Cardiology AI analyzes ECG and echocardiograms for arrhythmia. Predicts heart disease risk using patient data.

12. AI in Drug Discovery Accelerates drug development using data-driven models. Predicts molecular interactions and outcomes. Reduces cost and time.

13. AI in Personalized Medicine Tailors treatments based on genetic and clinical data. Improves therapeutic outcomes and reduces side effects.

14. AI in Epidemiology Predicts disease outbreaks using big data. Supports real-time surveillance and control .

15. AI in Hospital Management Optimizes patient flow and scheduling. Improves administrative efficiency and cost reduction.

16. AI in Medical Education Virtual simulations for training. AI tutors for personalized learning experiences.

17. Advantages of AI in Medicine Increased efficiency and accuracy. 24/7 availability with no fatigue. Supports evidence-based medicine .

18. Limitations and Challenges Data privacy and ethical concerns. Bias in algorithms due to poor data quality. High implementation cost and training requirements.

19. Ethical Issues Patient consent and data security. Transparency in AI decision-making. Regulatory and legal challenges.

20. Future of AI in Medicine Integration with genomics and wearable technology. Advancements in autonomous diagnostics. AI-driven personalized treatment plans.

21. AI and Human Collaboration AI supports—not replaces—healthcare professionals. Enhances decision-making through data insights.

22. Case Studies Google DeepMind: Eye disease detection. IBM Watson: Oncology treatment recommendations. AI in COVID-19 prediction and vaccine research.

23. AI Tools in Medicine TensorFlow, PyTorch for medical imaging AI. AI-enabled diagnostic platforms like Aidoc , PathAI .

24. Regulations and Guidelines WHO and FDA working on AI ethics frameworks. Emphasis on safety, transparency, and accountability.

25. Conclusion AI is transforming the medical field. Responsible and ethical use will enhance healthcare quality. AI + Human intelligence = Better patient outcomes.

26. References WHO. Artificial Intelligence in Health: 2023 Report. Nature Medicine, The Lancet Digital Health (2024). Stanford AI in Healthcare Initiative.