Enhance Healthcare Chatbot Transparency

on YouTube ↗09m

" summary

Claims concrete steps toward transparent healthcare chatbots: 24/7 symptom triage and context retention for chronic care, use of LIME and SHAP plus counterfactuals and layerwise propagation to explain outputs, backing models with knowledge graphs and continual deep learning, production data stack with ETL, data lakes and real-time streaming ETL pipelines, and attention to regulatory binds like HIPAA compliance and clinical validation so recommendations remain auditable and medically grounded.

AI-written summary · about

" chapters (7)

1. 0:00Introduction to Healthcare Chatbots and Explainable AI
2. 0:39Transforming Healthcare with AI Chatbots
3. 2:03Generative AI in Healthcare Chatbots
4. 3:41The Importance of Explainable AI
5. 6:22Data Engineering for AI Chatbots
6. 7:54Ethical Considerations and Future Challenges
7. 8:31Conclusion and Final Thoughts

" related talks

• 32mHow we use AI to deliver Telehealth at TranscarentPraful Kaul · Elevate 2023
• 50mMedical Chatbot and a Physician Rating SystemIlya Larchenko · HighLoad++ Moscow 2018
• 01h 00mKeynote Panel: AI in HealthcareSrini Gurrapu, Rohan Dsouza, Cyril Philip, Maryam Gholami, Michael Calhoun · Global Artificial Intelligence Virtual Conference Sep 2020
• 10mPrivacy and Respectful Discourse in AI ChatbotsJayati Dev · PEPR '22
• 46mStream-enabled chronic disease management at population scaleChet Yong, Ngiam Kee Yuan · AWS Summit Singapore 2022
• 26mHumanity and AIBeverly Wright, Eileen Oriel · Optimized AI Conference 2025
• 47mThe Future of Healthcare and AIMike Sicilia · Oracle Health Summit 2024
• 29mAI for COVID-19: An online virtual care approachXavier Amatriain · MLconf Online 2020