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1 - 10 of 88 results for: MED ; Currently searching winter courses. You can expand your search to include all quarters

MED 22N: Getting ahead of bias in AI applications

Artificial Intelligence (AI), the science and engineering of achieving tasks historically associated with human intelligence, is everywhere. As it rapidly evolves, staying ahead of ethical challenges is a new frontier. Society will need philosophers, ethicists, and creatives to have or partner with computer science and engineering skillsets to face the new realities that emerge as machines can learn from data to guide and make decisions. Nowhere is this more true than in the delivery of healthcare. In healthcare, AI is already filling roles like helping clinicians better identify frail patients at risk of poor health outcomes. But how can AI be a better part of the team? And whose responsibility is it when AI gets it wrong? In this course, we will take examples across the human life span and explore how AI can enhance human quality of life and experience. We will also explore where disparities in access to AI hinder outcomes and where bias in AI algorithms can lead to inappropriate dif more »
Artificial Intelligence (AI), the science and engineering of achieving tasks historically associated with human intelligence, is everywhere. As it rapidly evolves, staying ahead of ethical challenges is a new frontier. Society will need philosophers, ethicists, and creatives to have or partner with computer science and engineering skillsets to face the new realities that emerge as machines can learn from data to guide and make decisions. Nowhere is this more true than in the delivery of healthcare. In healthcare, AI is already filling roles like helping clinicians better identify frail patients at risk of poor health outcomes. But how can AI be a better part of the team? And whose responsibility is it when AI gets it wrong? In this course, we will take examples across the human life span and explore how AI can enhance human quality of life and experience. We will also explore where disparities in access to AI hinder outcomes and where bias in AI algorithms can lead to inappropriate differential treatment of patients. We will use qualitative interview methods as prework to learn approaches for getting ahead of bias in the development of AI. We will also explore principles of co-design, and consider how to bring the perspectives of patients and caregivers into the development of future AI approaches.
Terms: Win | Units: 3

MED 53Q: Storytelling in Medicine (LIFE 53Q)

Stories are at the core of medical practice, but the skills developed are applicable across disciplines, including technology and business. Storytelling in Medicine is a new sophomore seminar designed to teach skills in multiple modalities of storytelling including narrative, oral, social media, academic presentations and visual storytelling for different audiences. This seminar combines small groups, interactive workshops, and guest speakers who are experts in their fields of medicine. This will also include editing and support to complete your own story by the end of the seminar.
Terms: Win | Units: 3 | UG Reqs: WAY-CE
Instructors: Lin, B. (PI)

MED 73N: Scientific Method and Bias

Offers an introduction to the scientific method and common biases in science. Examines theoretical considerations and practical examples where biases have led to erroneous conclusions, as well as scientific practices that can help identify, correct or prevent such biases. Additionally focuses on appropriate methods to interweave inductive and deductive approaches. Topics covered include: Popper¿s falsification and Kuhn¿s paradigm shift, revolution vs. evolution; determinism and uncertainty; probability, hypothesis testing, and Bayesian approaches; agnostic testing and big data; team science; peer review; replication; correlation and causation; bias in design, analysis, reporting and sponsorship of research; bias in the public perception of science, mass media and research; and bias in human history and everyday life. Provides students an understanding of how scientific knowledge has been and will be generated; the causes of bias in experimental design and in analytical approaches; and the interactions between deductive and inductive approaches in the generation of knowledge.
Terms: Win | Units: 3 | UG Reqs: WAY-SMA

MED 114: Frontier Technology: Understanding and Preparing for Technology in the Next Economy (CEE 114, CEE 214, MED 214, PSYC 114)

The next wave of technological innovation and globalization will affect our countries, our societies, and ourselves. This interdisciplinary course provides an introduction to emerging, frontier technologies. Topics covered include artificial intelligence, additive manufacturing and advanced robotics, smart cities and urban mobility, telecommunications with 5G/6G, and other key emerging technologies in society. These technologies have vast potential to address the largest global challenges of the 21st century, ushering in a new era of progress and change.
Terms: Win | Units: 1
Instructors: Fischer, M. (PI)

MED 121: Translational Research and Applied Medicine (MED 221)

(Same as MED 121; undergraduate students enroll in MED 121) Open to graduate students and medical students, this course enables students to learn basic principles in the design, performance and analysis of translational medical research studies. The course includes both didactic seminars from experts in translational medicine as well as the opportunity to design and present a translational research project. Students enrolling for 3 units are paired with a TRAM translational research project and work as a team with TRAM trainees and faculty on a weekly basis, as arranged by the instructor, and present a final project update at the end of the quarter. MTRAM students must enroll for a letter grade.
Terms: Aut, Win, Spr | Units: 2

MED 160: Physician Shadowing: Stanford Immersion in Medicine Series (SIMS)

Undergraduates are paired with a physician mentor at Stanford Hospital and Clinics, Lucile Packard Children's Hospital, or the Veteran's Administration Hospital. May be repeated for credit. Prerequisite: Application and acceptance to the SIMS program.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit

MED 180: Artificial Intelligence in Medicine and Healthcare Ventures (PSYC 180)

The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence (AI), are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. However, few ever reach the patient - those with much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical know-how to integrate AI solutions into the medical system successfully. In this course, we tackle the central question: How can young students find feasible and impactful medical problems, and build, scale, and translate technology solutions into the clinic? Together, we will discover the transformative technologies of tomorrow that we can build today. Please see the syllabus for more information (https://t.ly/PpM2). We encourage students of all academic backgrounds to enroll; the only prerequisite is a strong passion for technology in healthcare. Course may be taken for on more »
The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence (AI), are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. However, few ever reach the patient - those with much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical know-how to integrate AI solutions into the medical system successfully. In this course, we tackle the central question: How can young students find feasible and impactful medical problems, and build, scale, and translate technology solutions into the clinic? Together, we will discover the transformative technologies of tomorrow that we can build today. Please see the syllabus for more information (https://t.ly/PpM2). We encourage students of all academic backgrounds to enroll; the only prerequisite is a strong passion for technology in healthcare. Course may be taken for one unit (lecture only, 11:30AM-12:30PM); or two units, which entails attending discussion section (12:30PM-1:20PM) and completing a project. The second half of each session will involve a discussion about team building, AI/Healthcare business ideas, and idea presentations. Grading criteria for 1-credit students will be based on attendance and weekly reports regarding the summary of each week's lectures (assignments). In addition to these criteria, 2-credit students will submit a business idea report and will deliver a pitch presentation in the last session in front of an invited panel.
Terms: Win | Units: 1-2
Instructors: Adeli, E. (PI)

MED 181: Preparation for Early Clinical Experience at the Cardinal Free Clinics

Training course for new undergraduate volunteers at the Cardinal Free Clinics (CFCs). Topics include an introduction to methods for providing culturally appropriate, high-quality transitional medical care for underserved patient populations, clinic structure and roles, free clinics in the larger context of American healthcare, foundations in community health, cultural humility and implicit bias in healthcare, motivational interviewing and patient advocacy skills, and role-specific preparation. Application only; must be an accepted CFC volunteer. Visit https://cfc.stanford.edu for more information. 1-2 units . Note: In-person attendance is required to participate in MED 181. Exceptions will only be made for students from the Office of Undergraduate Accommodations. (Cardinal Course certified by the Haas Center for Public Service)
Terms: Win | Units: 1-2

MED 182: Early Clinical Experience at the Cardinal Free Clinics (MED 282)

The Cardinal Free Clinics (CFCs), consisting of Arbor and Pacific Free Clinic, provide culturally appropriate, high quality transitional medical care for underserved patient populations in the Bay Area. Students volunteer in various clinic roles to offer services including health education, interpretation, referrals, and labs. In clinic students are guided in the practice of medical interviews, history-taking and physical examinations as appropriate, and work with attending physicians to arrive at a diagnosis and management plan. In addition, the CFC program follows continuous quality improvement. Visit http://cfc.stanford.edu for more information. For questions related to the course or volunteering, please email arborclinic@stanford.edu and/or pacific@ med.stanford.edu. Application only; must be an accepted CFC volunteer. (Cardinal Course certified by the Haas Center for Public Service.)
Terms: Aut, Win, Spr, Sum | Units: 1-2 | Repeatable for credit

MED 199: Undergraduate Research

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit
Instructors: Aalami, O. (PI) ; Advani, R. (PI) ; Ahmed, A. (PI) ; Ahuja, N. (PI) ; Alexander, K. (PI) ; Alizadeh, A. (PI) ; Andrews, J. (PI) ; Annes, J. (PI) ; Arai, S. (PI) ; Artandi, M. (PI) ; Artandi, S. (PI) ; Asch, S. (PI) ; Ashley, E. (PI) ; Assimes, T. (PI) ; Baiocchi, M. (PI) ; Banerjee, S. (PI) ; Barry, M. (PI) ; Basaviah, P. (PI) ; Basina, M. (PI) ; Bendavid, E. (PI) ; Berube, C. (PI) ; Bhalla, V. (PI) ; Bhatt, A. (PI) ; Bhattacharya, J. (PI) ; Blackburn, B. (PI) ; Blish, C. (PI) ; Bloom, G. (PI) ; Bollyky, P. (PI) ; Boxer, L. (PI) ; Brown, W. (PI) ; Chan, D. (PI) ; Chang, S. (PI) ; Chaudhuri, O. (PI) ; Chen, A. (PI) ; Chen, I. (PI) ; Chertow, G. (PI) ; Cheung, R. (PI) ; Chi, J. (PI) ; Chu, G. (PI) ; Chua, K. (PI) ; Chung, L. (PI) ; Clarke, M. (PI) ; Clusin, W. (PI) ; Colevas, A. (PI) ; Corsello, S. (PI) ; Dash, R. (PI) ; Daugherty, T. (PI) ; Dawson, L. (PI) ; Deresinski, S. (PI) ; Desai, M. (PI) ; Desai, T. (PI) ; Dhillon, G. (PI) ; Dosiou, C. (PI) ; Downing, N. (PI) ; DuBose, A. (PI) ; Edwards, L. (PI) ; Einav, S. (PI) ; Fantl, W. (PI) ; Fearon, W. (PI) ; Felsher, D. (PI) ; Fisher, G. (PI) ; Ford, J. (PI) ; Ford, P. (PI) ; Frank, M. (PI) ; Frayne, S. (PI) ; Friedland, S. (PI) ; Gabiola, J. (PI) ; Ganjoo, K. (PI) ; Gardner, C. (PI) ; Gardner, P. (PI) ; Geng, L. (PI) ; Gesundheit, N. (PI) ; Glaseroff, A. (PI) ; Glenn, J. (PI) ; Goldhaber-Fiebert, J. (PI) ; Goodman, S. (PI) ; Gotlib, J. (PI) ; Habtezion, A. (PI) ; Harman, S. (PI) ; Heaney, C. (PI) ; Heidenreich, P. (PI) ; Henri, H. (PI) ; Hernandez-Boussard, T. (PI) ; Ho, D. (PI) ; Hoffman, A. (PI) ; Holodniy, M. (PI) ; Ioannidis, J. (PI) ; Jernick, J. (PI) ; Ji, H. (PI) ; Johnston, L. (PI) ; Jones, E. (PI) ; Kalbasi, A. (PI) ; Kao, P. (PI) ; Kastelein, M. (PI) ; Katz, R. (PI) ; Kenny, K. (PI) ; Khatri, P. (PI) ; Khush, K. (PI) ; Kim, S. (PI) ; King, A. (PI) ; Knowles, J. (PI) ; Kraemer, F. (PI) ; Kuo, C. (PI) ; Kuo, C. (PI) ; Kurian, A. (PI) ; Kurtz, D. (PI) ; Kuschner, W. (PI) ; Ladabaum, U. (PI) ; Lafayette, R. (PI) ; Laws, A. (PI) ; Lee, D. (PI) ; Lee, J. (PI) ; Leung, L. (PI) ; Levin, E. (PI) ; Levy, R. (PI) ; Liedtke, M. (PI) ; Lin, B. (PI) ; Lorenz, K. (PI) ; Lowsky, R. (PI) ; Luby, S. (PI) ; Lunn, M. (PI) ; Majeti, R. (PI) ; McConnell, M. (PI) ; McLaughlin, T. (PI) ; Medeiros, B. (PI) ; Mercola, M. (PI) ; Miklos, D. (PI) ; Miller, G. (PI) ; Milstein, A. (PI) ; Mitchell, B. (PI) ; Mohabir, P. (PI) ; Morioka-Douglas, N. (PI) ; Musen, M. (PI) ; Narayan, S. (PI) ; Neal, J. (PI) ; Negrin, R. (PI) ; Nevins, A. (PI) ; Nguyen, L. (PI) ; Nguyen, M. (PI) ; Nguyen, P. (PI) ; Nicolls, M. (PI) ; Nieman, K. (PI) ; Obedin-Maliver, J. (PI) ; Osterberg, L. (PI) ; Owens, D. (PI) ; Palaniappan, L. (PI) ; Pao, A. (PI) ; Parikh, V. (PI) ; Parsonnet, J. (PI) ; Pegram, M. (PI) ; Periyakoil, V. (PI) ; Phadke, A. (PI) ; Pinto, H. (PI) ; Pompei, P. (PI) ; Price, E. (PI) ; Prochaska, J. (PI) ; Quertermous, T. (PI) ; Rehkopf, D. (PI) ; Relman, D. (PI) ; Robinson, B. (PI) ; Rockson, S. (PI) ; Rodriguez, F. (PI) ; Rohatgi, R. (PI) ; Rosas, L. (PI) ; Rosser, J. (PI) ; Ruoss, S. (PI) ; Rydel, T. (PI) ; Schnittger, I. (PI) ; Schroeder, J. (PI) ; Shafer, R. (PI) ; Shah, N. (PI) ; Shah, S. (PI) ; Shah, MD (SHC Chief of Staff), J. (PI) ; Sharp, C. (PI) ; Shen, K. (PI) ; Shieh, L. (PI) ; Shizuru, J. (PI) ; Shoor, S. (PI) ; Singer, S. (PI) ; Singh, B. (PI) ; Singh, U. (PI) ; Skeff, K. (PI) ; Spiekerkoetter, E. (PI) ; Srinivas, S. (PI) ; Srinivasan, M. (PI) ; Stafford, R. (PI) ; Stefanick, M. (PI) ; Studdert, D. (PI) ; Tai, J. (PI) ; Tamang, S. (PI) ; Tamura, M. (PI) ; Tan, J. (PI) ; Telli, M. (PI) ; Tepper, R. (PI) ; Tompkins, L. (PI) ; Tremmel, J. (PI) ; Tsao, P. (PI) ; Utz, P. (PI) ; Vagelos, R. (PI) ; Valantine, H. (PI) ; Verghese, A. (PI) ; Wakelee, H. (PI) ; Wang, P. (PI) ; Warvariv, V. (PI) ; Weinacker, A. (PI) ; Weng, K. (PI) ; Weng, W. (PI) ; Wheeler, M. (PI) ; Winslow, D. (PI) ; Witteles, R. (PI) ; Wu, J. (PI) ; Wu, J. (PI) ; Wu, S. (PI) ; Yang, P. (PI) ; Yeung, A. (PI) ; Yock, P. (PI) ; Zamanian, R. (PI) ; Zehnder, J. (PI) ; Zhu, H. (PI) ; Zulman, D. (PI) ; de Jesus Perez, V. (PI)
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