School of Medicine
Showing 31-40 of 50 Results
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Lawrence Chu, MD, MS
Professor of Anesthesiology, Perioperative and Pain Medicine
Current Research and Scholarly InterestsI have two lines of research, one involving educational informatics and use of technology in postgraduate medical education and another involving NIH-funded work in patient-oriented clinical research regarding opioid use and physiologic responses associated with acute and chronic exposure in humans.
For a full description of my educational informatics work, please see my website aim.stanford.edu.
My clinical research focuses on the study opiate-induced hyperalgesia in patients suffering from chronic pain.
I am currently conducting an NIH-funded five year double-blinded randomized controlled clinical study (NIGMS award 1K23GM071400-01) that prospectively examines the following hypotheses: 1) pain patients on chronic opioid therapy develop dose-dependent tolerance and/or hyperalgesia to these medications over time, 2) opiate-induced tolerance and hyperalgesia develop differently with respect to various types of pain, 3) opioid-induced hyperalgesia occurs independently of withdrawal phenomena, and 4) opiate-induced tolerance and hyperalgesia develop differently based on gender and/or ethnicity.
The study is the first quantitative and prospective examination of tolerance and hyperalgesia in pain patients and may have important implications for the rational use of opioids in the treatment of chronic pain. -
Philip Chung
Instructor, Anesthesiology, Perioperative and Pain Medicine
BioI am a general anesthesiologist and physician-scientist with prior training as an engineer. My areas of research include clinical informatics, natural language processing, machine learning, and artificial intelligence applied to perioperative medicine and anesthesiology. Currently I am a postdoctoral fellow in Nima Aghaeepour's laboratory. See my CV, Biosketch, and Google Scholar on the bottom right of this page for more information.
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David Clark
Professor of Anesthesiology, Perioperative and Pain Medicine
BioMy career is dedicated to improving the safety, effectiveness and availability of pain relief. Both the needs and opportunities in these areas are limitless. I have had the good fortune of working as a clinician, teacher and scientist at Stanford University and the Palo Alto VA hospital for more than two decades.
Much of my time is spent on laboratory, translational and clinical research. In the laboratory, we are pursuing several projects related to the questions of why pain sometimes becomes chronic after injuries and why opioids lose their effectiveness over time. Alterations in endogenous pain control mechanisms and the involvement of the adaptive system of immunity are central to these investigations. We would like to find ways to maximize functional recovery after surgery and other forms of trauma while minimizing the risks of analgesic use. This work involves local, national and international collaborations. Clinical trials work involves establishing the efficacy of novel forms of analgesic therapy as well as the comparative effectiveness of long-established approaches to controlling common forms of pain such as low back pain. This spectrum of pain-related pursuits continues to evolve with the rapid expansion of the field. -
Bernard Mawuli Cobbinah
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioCobbinah Bernard Mawuli is a Postdoctoral Scholar at Stanford University in the Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine. He is passionate about the intersection of AI and medicine, focusing on developing robust and effective approaches for preventive and predictive healthcare. His research aims to deepen the understanding of high-dimensional multi-omics medical data using advanced machine learning techniques. By exploring innovative ways to analyze this data, his work contributes to improved treatments and enhanced patient care. Through the analysis of large patient datasets, his goal is to create tools that empower clinicians to make more informed decisions, ultimately improving healthcare outcomes for all.
Prior to joining Stanford, he pioneered robust federated learning techniques for evolving data streams and developed methods to reduce multi-center MRI variability in diagnosing brain disorders.
