Bilal Naved,PhD
Icahn School of Medicine at Mount Sinai
Bilal A. Naved, PhD, is a Faculty member in the Windreich Department of AI in Human Health at the Icahn School of Medicine at Mount Sinai as a Schmidt AI Fellow through the Eric & Wendy Schmidt AI in Human Health Fellowship. He is also the Co-Founder and Chief Product Officer of Clearstep, a venture-backed health technology company focused on clinically intelligent patient navigation and access.
Naved has built and scaled what has been published in Nature Digital Medicine as the most widely implemented AI agent for clinically intelligent patient navigation among U.S. health systems. Clearstep’s patient-facing “clinical processing unit” powers self-triage and navigation across 15+ major U.S. health systems, touching patients in all 50 states including Mount Sinai Health System, HCA, Ochsner Health, Baylor Scott & White Health, BayCare, Novant Health, Tufts Medicine, Cooper University Healthcare, and the U.S. Military’s Defense Health Agency. Clearstep was selected by the Defense Health Agency as the A.I. agent to power the clinical and machine intelligence of its Digital Front Door+ application. In real-world deployments, the agent clarifies care pathways, redirects clinically inappropriate intents, automates unnecessary use of nurse call centers, and optimizes provider capacity, with deployments supporting emergency escalation and life-saving interventions.
Trained as a physician-scientist in Northwestern University’s Medical Scientist Training Program (MD/PhD candidate), Naved completed a PhD in Biomedical Engineering and pursued additional post-doctoral training (Dec 2024–Nov 2025) with Northwestern University’s Chief AI Officer, Yuan Luo, focused on patient-facing A.I. agents for clinically intelligent self-triage and navigation. Across his academic work, he has been supported by NIH F30 funding and published research spanning digital health, clinical NLP, chronic kidney disease and organ engineering, gait analysis, genetics, and biomaterials.
Naved’s work has been recognized by KLAS (Emerging Solutions) and Forbes (Next 1000), among other national innovation programs. At Mount Sinai, he is building a first-of-its-kind bridge between health tech and academic medicine to rigorously evaluate patient-facing AI agents through multi-center, prospective clinical studies and to develop evidence-driven foundations for learning health systems that improve access, safety, and efficiency at scale.
Naved has built and scaled what has been published in Nature Digital Medicine as the most widely implemented AI agent for clinically intelligent patient navigation among U.S. health systems. Clearstep’s patient-facing “clinical processing unit” powers self-triage and navigation across 15+ major U.S. health systems, touching patients in all 50 states including Mount Sinai Health System, HCA, Ochsner Health, Baylor Scott & White Health, BayCare, Novant Health, Tufts Medicine, Cooper University Healthcare, and the U.S. Military’s Defense Health Agency. Clearstep was selected by the Defense Health Agency as the A.I. agent to power the clinical and machine intelligence of its Digital Front Door+ application. In real-world deployments, the agent clarifies care pathways, redirects clinically inappropriate intents, automates unnecessary use of nurse call centers, and optimizes provider capacity, with deployments supporting emergency escalation and life-saving interventions.
Trained as a physician-scientist in Northwestern University’s Medical Scientist Training Program (MD/PhD candidate), Naved completed a PhD in Biomedical Engineering and pursued additional post-doctoral training (Dec 2024–Nov 2025) with Northwestern University’s Chief AI Officer, Yuan Luo, focused on patient-facing A.I. agents for clinically intelligent self-triage and navigation. Across his academic work, he has been supported by NIH F30 funding and published research spanning digital health, clinical NLP, chronic kidney disease and organ engineering, gait analysis, genetics, and biomaterials.
Naved’s work has been recognized by KLAS (Emerging Solutions) and Forbes (Next 1000), among other national innovation programs. At Mount Sinai, he is building a first-of-its-kind bridge between health tech and academic medicine to rigorously evaluate patient-facing AI agents through multi-center, prospective clinical studies and to develop evidence-driven foundations for learning health systems that improve access, safety, and efficiency at scale.
