AI_in_Healthcare_Presentationhhhhhh.pptx

This presentation explains how Artificial Intelligence (AI) is transforming healthcare through automation, data analysis, and decision-making. It discusses applications, research progress, and challenges.

🔹 Key Sections Summary

1. Introduction
Defines AI in healthcare and explains its purpose — using algorithms to improve diagnostics, treatment, and patient management.

2. History and Evolution
Traces the journey from early expert systems (like MYCIN) to modern deep learning and robotic surgeries, highlighting major technological milestones.

3. AI in Diagnostics
Explains machine learning models that detect diseases from X-rays, MRIs, and pathology images. AI improves speed and accuracy in early disease detection.

4. AI in Treatment Planning
Covers robotic surgeries, personalized medicine, and AI models predicting treatment outcomes or drug effectiveness.

5. AI in Patient Care and Monitoring
Describes wearables and IoT-enabled devices that track patient vitals, predict health risks, and improve preventive care. Chatbots assist patients 24/7.

6. Ethical and Legal Challenges
Discusses privacy, data security, algorithmic bias, and lack of regulatory clarity in healthcare AI systems.

7. Future Scope
Focuses on integration of AI with genomics, biotechnology, and global telemedicine platforms, aiming for “AI-powered hospitals.”

8. Conclusion
Summarizes benefits like accuracy and efficiency but stresses the need for ethical governance and transparency in future AI healthcare systems.

1. Artificial Intelligence in Healthcare – Research Insights & Future Scope An Informative Research-Based Presentation

2. Introduction Definition of Artificial Intelligence (AI) in Healthcare Overview of AI applications in medical diagnostics and treatment Purpose and scope of research

3. History and Evolution Early development of AI in medical systems Milestones in healthcare automation and machine learning Integration with clinical decision-making systems

4. AI in Diagnostics Machine learning in image recognition (X-rays, MRI) AI-based disease prediction models Accuracy improvements and case examples

5. AI in Treatment Planning AI-assisted surgery and robotic systems Personalized medicine using AI algorithms Drug discovery through predictive modeling

6. AI in Patient Care and Monitoring Wearable technology and IoT integration Predictive health alerts and chronic disease management AI chatbots in patient engagement

7. Ethical and Legal Challenges Privacy and data protection concerns AI bias in medical decisions Regulatory and ethical frameworks

8. Future Scope AI in genomics and biotechnology Potential for full-scale AI hospitals Integration with global health systems

9. Conclusion Summary of AI benefits in healthcare Challenges to overcome Future outlook and research directions