School of Medicine
Showing 381-390 of 1,308 Results
-
Bobak Seddighzadeh
Affiliate, Department Funds
Fellow in Peds/Clinical InformaticsBioOver 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
Postdoctoral Scholar, Immunology and Rheumatology
BioDr. Mojtaba Fazli is a leading scientist specializing in AI/ML, computer vision, and biomedical research. He is currently a Postdoctoral Research fellow Scientist at Stanford University and a Senior Research Fellow at the Harvard Ophthalmology Artificial Intelligence Lab, Harvard University, where he previously completed a postdoctoral fellowship.
Dr. Fazli's research bridges cutting-edge artificial intelligence with groundbreaking applications in multi-scale biomedical imaging, disease modeling, and drug discovery. His expertise encompasses advanced areas of AI/ML, including computer vision for 2D/3D medical image analysis, bioinformatics, and object tracking in both 2D and 3D environments. He has played a key role in developing state-of-the-art algorithms to enhance diagnostic precision and therapeutic outcomes within the biotechnology and healthcare sectors.
With a strong foundation in both academia and industry, Dr. Fazli previously served as a Senior Open Innovation Scholar at the Novartis Institute for Biomedical Research. There, he applied his expertise in strategic planning, programming, and simulation to tackle complex biomedical challenges.
Dr. Fazli holds a Ph.D. in Computer Science, with a minor in Mathematics, from the United States, as well as a Doctorate in Business Administration from France. His academic journey also includes master’s degrees in Economics and Management, as well as Artificial Intelligence and Robotics. His interdisciplinary approach blends AI-driven innovation with practical, impactful solutions in healthcare.
At Stanford, Dr. Fazli leads research initiatives focused on integrating multimodal data in rheumatology, advancing ultrasound imaging research in Rheumatoid Arthritis, and developing AI methodologies for clinical applications. His current work also involves leveraging Generative AI and Large Language Models (LLMs) to drive innovation in medical data analysis and clinical decision support.
