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Sneha Jain
Stanford Health Care, Stanford, US
Biography
Dr Sneha Shah Jain is Clinical Assistant Professor of Medicine and Director of the GUIDE-AI Lab at Stanford Health Care, US.
She specialises in cardiovascular medicine and preventive cardiology. Her academic background includes an Economics degree with distinction from Duke University, a medical degree from Johns Hopkins School of Medicine and an MBA from Harvard Business School. She completed internal medicine residency at Columbia/NewYork-Presbyterian and cardiovascular fellowship at Stanford University.
Dr Jain leads research in artificial intelligence (AI) for cardiovascular care, focusing on safe, evidence-based implementation to enhance patient outcomes. She collaborates with the Data Science Team at Stanford Healthcare and the Stanford Center for Clinical Research to deploy and prospectively evaluate clinical AI tools.
She holds leadership roles in national cardiovascular innovation, serving on the American College of Cardiology Healthcare Innovation Section council, the American Heart Association Expert Panel for the AI Validation Lab and as an expert AI consultant for the Journal of the American College of Cardiology.
Featured Courses
- 1.00
Published:
Learning objectives
- Discuss the unmet needs in thrombosis management, MACE burden after ACS, stroke in AF and secondary stroke prevalence and consequences
- Evaluate the current standard of care for the treatment of ACS, SSP and AF and its limitations
- Review guidelines and critique current prescribing practices and the implications of inadequate treatment
- Explain the different strategies for targeting FXI and how FXI inhibition mechanisms differentiate from traditional anticoagulation therapies
- Identify emerging anticoagulant strategies and describe the efficacy and safety data from Phase 2 FXI inhibitor clinical trials as well as ongoing Phase 3 trials and potential place in therapy for ACS, AF and SSP
- Apply evidence-based anticoagulation strategies by optimising treatment decisions and addressing patient-specific risk factors