The Impact of AI and ML in trauma care.pptx

The Impact of AI and ML in Trauma Care

Current Applications

1. Triage: AI algorithms assist in rapid patient assessment, predicting outcomes and resource needs.

2. Imaging Analysis: AI systems quickly analyze medical images, detecting subtle injuries.

3. Predictive Analytics: ML models forecast complications, enabling proactive care.

4. Decision Support: AI provides real-time treatment recommendations based on best practices.

Future Potential

1. Personalized Treatment: AI will generate individualized care plans considering genetic profiles.

2. Real-time Monitoring: Advanced systems will continuously analyze patient data, predicting complications.

3. Robotic Surgery Assistance: AI-powered robots could enhance precision in trauma surgeries.

4. VR/AR Training: AI-driven simulations will offer adaptive training for medical professionals.

5. Post-Trauma Care Prediction: ML models will guide long-term rehabilitation planning.

Challenges

1. Data Quality: Consistent, high-quality data collection is crucial for reliable AI models.

2. Workflow Integration: AI must seamlessly fit into fast-paced trauma care environments.

3. Ethical and Legal Issues: Accountability, privacy, and potential biases need addressing.

4. Validation: Clinical trials are necessary to prove AI systems' efficacy in real-world settings.

5. Provider Education: Building trust and understanding among healthcare providers is essential.

6. Resource Allocation: Ensuring equitable access to AI technologies across healthcare settings.

Conclusion

AI and ML have the potential to transform trauma care, enhancing diagnosis, treatment, and outcome prediction. Successful implementation requires addressing challenges in data management, ethics, and integration. The future of trauma care lies in the synergy between human expertise and artificial intelligence, promising more advanced, personalized, and effective patient care.

1. The Impact of AI and ML in Trauma care Dr. Chaitanya Kondabala MS(GS), MCh . (Trauma Surgery And Critical Care)

2. Introduction

3. Subsets of AI in Trauma Care

4. Real Examples

5. Real-time AI systems in healthcare Clinical Decision Support Systems – Epic’s Cognitive Computing platform Automated Image analysis – Aidoc for real time CT scan analysis Predictive analytics for patient monitoring- Excel Medical WAVE Clinical Platform Natural Language Processing for EHR – Nuance’s Dragon Medical One

6. Indication--34/M pain after a boxing lesson Results fracture of the hamatum seen by Bone View and confirmed by CT

7. Case studies of successful AI implementation in trauma centers IBM WATSON HEALTH

8. Parkland Trauma Index of Mortality

9. Cost benefit Analysis of implementing AI in Trauma care Costs : • Initial investment • Training and upskilling • Data infrastructure • Ongoing maintenance Benefits • Faster diagnosis • Reduced human error • Improved resource allocation • Enhanced patient outcomes Long-term Impact : • Decreased mortality rates • Reduced healthcare costs • Improved operational efficiency • Advanced research capabilities Challenges • ROI measurement • Integration with existing systems • Ethical considerations Key Considerations • Scalability • Adaptability to local context • Continuous evaluation

10. Patient privacy and Data Security concerns

11. Regulatory landscape and approval processes for AI in healthcare

12. Training requirements for healthcare professionals to use AI tools

13. Future direction and emerging technologies Advanced predictive models AI-powered robotic surgery assistants Personalized treatment plans based on genetic data AR for real-time surgical guidance AI-driven emergency response system Wearable AI for continuous patient monitoring Integration of IoT devices for holistic patient data

14. AI/ML in trauma care: Key Collaborators Key activities : Identify Needs  Develop Solutions Implement & train Monitor & Improve

15. Key takeaways AI transforms trauma care: Improved triage, diagnosis, treatment Real-world impact: 98% sensitivity (hip fractures) 65.7% faster fracture detection AI systems enhancing diagnostics: BoneView, VeriScout Long-term benefits: Lower mortality rates Improved efficiency Challenges: Multicenter validation Data privacy System integration Evolving regulations: National Strategy for AI (India) Ongoing training crucial Future: AI-powered surgery Personalized treatments Collaboration key: Healthcare, AI experts, admins, ethicists Ethical implementation vital

16. Thank You The synergy of human compassion and machine precision : that's the promise of AI and ML in trauma care