School of Medicine
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Bobak Seddighzadeh
Fellow in Peds/Clinical Informatics
BioOver the past 13 years, Dr. Seddighzadeh has advanced biomedical innovation at Harvard, Stanford, and the Mayo Clinic, integrating emerging technologies with clinical medicine to improve patient care.
Dr. Seddighzadeh’s expertise spans genomic medicine, clinical informatics, and clinical AI. He has built enterprise-level clinical decision support systems that improve care at scale, and as part of the Stanford GUIDE-AI group and the Nigam Shah Lab, he focuses on developing AI-enabled clinical platforms for Stanford’s hospitals and clinics. His work in clinical AI includes implementation, evaluation, and safety guardrails. He also contributes to precision medicine efforts that use multi-omic data to identify disease subtypes and enable more individualized care. As part of Chan Zuckerberg Biohub, he helped build one of the world’s first complete human cell atlases.
In clinical practice, Dr. Seddighzadeh is committed to delivering outstanding internal medicine care to hospitalized patients. He approaches medicine as a craft, continually sharpening diagnostic reasoning and therapeutic decision-making in service of the best possible outcomes. He also values prevention and partners with patients to build sustainable habits that support long-term health and health span.
At New York University, Dr. Seddighzadeh received the Degree Representative Award, an honor conferred by the faculty recognizing the single graduating student with the highest overall academic achievement. He later earned a full-tuition scholarship from the founding dean to attend the University of Nevada, where he graduated with top honors in medicine. He went on to complete his internal medicine residency at Mayo Clinic where he was selected for the Resident Leadership Academy, a specialized program for residents identified across the Mayo Clinic enterprise as future leaders. There he also developed and launched the AI and Medicine Residency Track. He is currently a Clinical Informatics Fellow and internal medicine hospitalist at Stanford University. -
Mojtaba Sedigh Fazli
Visiting Instructor/Lecturer, Cardiovascular Institute
Postdoctoral Scholar, Cardiovascular InstituteBioDr. Mojtaba Fazli is a leading artificial intelligence scientist and Lecturer at the Stanford Cardiovascular Institute and Stanford School of Medicine. His research connects machine learning, biomedical imaging, and cardiovascular physiology to address a central challenge in clinical AI: translating complex biomedical data into interpretable insights that can support better clinical decisions.
Working under the mentorship of François Haddad and Suzanne Tamang, Dr. Fazli develops AI approaches that integrate electrocardiography, echocardiography, invasive hemodynamic measurements, and clinical data. His current research spans pulmonary hypertension, right-heart function, and the estimation of cardiovascular pressures and function from noninvasive measurements. A defining focus of his work is explainability—understanding not only what a model predicts, but also how its predictions relate to clinically meaningful physiological patterns. Through this work, he investigates how AI can complement established cardiovascular assessment and support more informative, accessible approaches to disease evaluation.
Before his current appointment, Dr. Fazli held successive postdoctoral positions in Stanford’s Division of Immunology and Rheumatology and at the Cardiovascular Institute. Under the supervision of Suzanne Tamang and Rob Fairchild, his rheumatology research focused on AI-enabled ultrasound analysis, rheumatoid arthritis assessment, and the integration of multimodal clinical data. This experience helped shape his broader research approach: connecting imaging, quantitative measurements, and clinical context to address questions that matter in patient care.
His background spans academic medicine and pharmaceutical research, including postdoctoral training and senior research experience at the Harvard Ophthalmology Artificial Intelligence Lab and Harvard AI and Robotics Lab. He also served as a Senior Open Innovation Scholar and Gates Fundation Fellow at the Novartis Institutes for BioMedical Research, applying computational methods to biomedical research and drug discovery. Across these settings, his work has encompassed computer vision, 2D and 3D biomedical image analysis, computational disease modeling, and AI-driven analysis of complex biological data.
Dr. Fazli’s broader research interests include multimodal learning, computer vision, agentic AI, generative AI, and large language models for medical data analysis, integration of clinical knowledge, and clinical decision support. Across these areas, he emphasizes rigorous evaluation, transparent modeling, and close collaboration between computational scientists and clinicians.
He holds a PhD in Computer Science, with a minor in Mathematics, from the University of Georgia, a Doctorate in Business Administration, and master’s degrees in Economics and Management and in Artificial Intelligence and Robotics. This interdisciplinary foundation informs his approach to developing AI that is technically rigorous, clinically grounded, and responsive to the practical challenges of healthcare.
