Ashwin Nayak M.D., M.S. is a Clinical Assistant Professor at the Stanford School of Medicine. He completed his M.D. at the University of Illinois at Chicago College of Medicine and Internal Medicine residency at Stanford. He completed his master's degree in Clinical Informatics Management at Stanford University and is board-certified in Clinical Informatics. He has a background in machine learning and app development, and has research interests in large language models, conversational AI and digital therapeutics. He currently practices as an academic hospitalist at Stanford.

Clinical Focus

  • Internal Medicine
  • Clinical Informatics

Academic Appointments

  • Clinical Assistant Professor, Medicine

Administrative Appointments

  • Section Chief, Med 7, Stanford University (2022 - Present)

Professional Education

  • Board Certification, American Board of Preventive Medicine, Clinical Informatics (2023)
  • MS, Stanford University, Clinical Informatics Management (2022)
  • Board Certification: American Board of Internal Medicine, Internal Medicine (2021)
  • Residency: Stanford University Internal Medicine Residency (2021) CA
  • Medical Education: University of Illinois College of Medicine (2018) IL

Current Research and Scholarly Interests

Conversational AI, Large Language Models, Digital Therapeutics

Clinical Trials

  • Managing Insulin With a Voice AI Not Recruiting

    This study randomizes participants to have their basal insulin titrated either through standard of care or by receiving prompts through interactions with an AI-enabled smart speaker device. The primary objective of this study is to investigate the feasibility of an AI-enabled smart speaker device and whether such a device facilitates insulin titration management, increases insulin adherence and decreases time to optimal insulin dose. The secondary objective of the study is to explore whether the device improves glycemic control as defined by improvements in fasting blood sugar.

    Stanford is currently not accepting patients for this trial. For more information, please contact Study Team, 650-308-8062.

    View full details

All Publications

  • Comparing IM Residency Application Personal Statements Generated by GPT-4 and Authentic Applicants. Journal of general internal medicine Nair, V., Nayak, A., Ahuja, N., Weng, Y., Keet, K., Hosamani, P., Hom, J. 2024

    View details for DOI 10.1007/s11606-024-08784-w

    View details for PubMedID 38689120

    View details for PubMedCentralID 10589311

  • Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine. NPJ digital medicine Savage, T., Nayak, A., Gallo, R., Rangan, E., Chen, J. H. 2024; 7 (1): 20


    One of the major barriers to using large language models (LLMs) in medicine is the perception they use uninterpretable methods to make clinical decisions that are inherently different from the cognitive processes of clinicians. In this manuscript we develop diagnostic reasoning prompts to study whether LLMs can imitate clinical reasoning while accurately forming a diagnosis. We find that GPT-4 can be prompted to mimic the common clinical reasoning processes of clinicians without sacrificing diagnostic accuracy. This is significant because an LLM that can imitate clinical reasoning to provide an interpretable rationale offers physicians a means to evaluate whether an LLMs response is likely correct and can be trusted for patient care. Prompting methods that use diagnostic reasoning have the potential to mitigate the "black box" limitations of LLMs, bringing them one step closer to safe and effective use in medicine.

    View details for DOI 10.1038/s41746-024-01010-1

    View details for PubMedID 38267608

    View details for PubMedCentralID 9931230

  • Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes: A Randomized Clinical Trial. JAMA network open Nayak, A., Vakili, S., Nayak, K., Nikolov, M., Chiu, M., Sosseinheimer, P., Talamantes, S., Testa, S., Palanisamy, S., Giri, V., Schulman, K. 2023; 6 (12): e2340232


    Optimizing insulin therapy for patients with type 2 diabetes can be challenging given the need for frequent dose adjustments. Most patients receive suboptimal doses and do not achieve glycemic control.To examine whether a voice-based conversational artificial intelligence (AI) application can help patients with type 2 diabetes titrate basal insulin at home to achieve rapid glycemic control.In this randomized clinical trial conducted at 4 primary care clinics at an academic medical center from March 1, 2021, to December 31, 2022, 32 adults with type 2 diabetes requiring initiation or adjustment of once-daily basal insulin were followed up for 8 weeks. Statistical analysis was performed from January to February 2023.Participants were randomized in a 1:1 ratio to receive basal insulin management with a voice-based conversational AI application or standard of care.Primary outcomes were time to optimal insulin dose (number of days needed to achieve glycemic control), insulin adherence, and change in composite survey scores measuring diabetes-related emotional distress and attitudes toward health technology and medication adherence. Secondary outcomes were glycemic control and glycemic improvement. Analysis was performed on an intent-to-treat basis.The study population included 32 patients (mean [SD] age, 55.1 [12.7] years; 19 women [59.4%]). Participants in the voice-based conversational AI group more quickly achieved optimal insulin dosing compared with the standard of care group (median, 15 days [IQR, 6-27 days] vs >56 days [IQR, >29.5 to >56 days]; a significant difference in time-to-event curves; P = .006) and had better insulin adherence (mean [SD], 82.9% [20.6%] vs 50.2% [43.0%]; difference, 32.7% [95% CI, 8.0%-57.4%]; P = .01). Participants in the voice-based conversational AI group were also more likely than those in the standard of care group to achieve glycemic control (13 of 16 [81.3%; 95% CI, 53.7%-95.0%] vs 4 of 16 [25.0%; 95% CI, 8.3%-52.6%]; difference, 56.3% [95% CI, 21.4%-91.1%]; P = .005) and glycemic improvement, as measured by change in mean (SD) fasting blood glucose level (-45.9 [45.9] mg/dL [95% CI, -70.4 to -21.5 mg/dL] vs 23.0 [54.7] mg/dL [95% CI, -8.6 to 54.6 mg/dL]; difference, -68.9 mg/dL [95% CI, -107.1 to -30.7 mg/dL]; P = .001). There was a significant difference between the voice-based conversational AI group and the standard of care group in change in composite survey scores measuring diabetes-related emotional distress (-1.9 points vs 1.7 points; difference, -3.6 points [95% CI, -6.8 to -0.4 points]; P = .03).In this randomized clinical trial of a voice-based conversational AI application that provided autonomous basal insulin management for adults with type 2 diabetes, participants in the AI group had significantly improved time to optimal insulin dose, insulin adherence, glycemic control, and diabetes-related emotional distress compared with those in the standard of care group. These findings suggest that voice-based digital health solutions can be useful for medication Identifier: NCT05081011.

    View details for DOI 10.1001/jamanetworkopen.2023.40232

    View details for PubMedID 38039007

  • Comparison of History of Present Illness Summaries Generated by a Chatbot and Senior Internal Medicine Residents. JAMA internal medicine Nayak, A., Alkaitis, M. S., Nayak, K., Nikolov, M., Weinfurt, K. P., Schulman, K. 2023

    View details for DOI 10.1001/jamainternmed.2023.2561

    View details for PubMedID 37459091

  • MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records arXiv Fleming, S. L. 2023
  • Reactivation of Chagas Disease in a Patient With an Autoimmune Rheumatic Disease: Case Report and Review of the Literature. Open forum infectious diseases Czech, M. M., Nayak, A. K., Subramanian, K. n., Suarez, J. F., Ferguson, J. n., Jacobson, K. B., Montgomery, S. P., Chang, M. n., Bae, G. H., Raghavan, S. S., Wang, H. n., Miranti, E. n., Budvytiene, I. n., Shoor, S. M., Banaei, N. n., Rieger, K. n., Deresinski, S. n., Holubar, M. n., Blackburn, B. G. 2021; 8 (2): ofaa642


    Reactivation of Chagas disease has been described in immunosuppressed patients, but there is a paucity of literature describing reactivation in patients on immunosuppressive therapies for the treatment of autoimmune rheumatic diseases. We describe a case of Chagas disease reactivation in a woman taking azathioprine and prednisone for limited cutaneous systemic sclerosis (lcSSc). Reactivation manifested as indurated and erythematous cutaneous nodules. Sequencing of a skin biopsy specimen confirmed the diagnosis of Chagas disease. She was treated with benznidazole with clinical improvement in the cutaneous lesions. However, her clinical course was complicated and included disseminated CMV disease and subsequent septic shock due to bacteremia. Our case and review of the literature highlight that screening for Chagas disease should be strongly considered for patients who will undergo immunosuppression for treatment of autoimmune disease if epidemiologically indicated.

    View details for DOI 10.1093/ofid/ofaa642

    View details for PubMedID 33575423

    View details for PubMedCentralID PMC7863873

  • Patient-centered design in developing a mobile application for oral anticancer medications JOURNAL OF THE AMERICAN PHARMACISTS ASSOCIATION Crawford, S. Y., Boyd, A. D., Nayak, A. K., Venepalli, N. K., Cuellar, S., Wirth, S. M., Hsu, G. 2019; 59 (2): S586-+


    To develop and test the usability and feasibility of a customizable mobile application (app) designed to help educate patients about their oral anticancer medications (OAMs) and regimens.Outpatient cancer center and oncology pharmacy for urban, Midwestern academic health system.Clinically-supervised educational intervention to support patients learning about OAMs.With input from patient partners, our interdisciplinary team designed the first known tablet-based educational app that can interface with a patient's electronic medical record. The app is based on learning style and adherence theories and is customizable for individually prescribed OAMs. The app can accommodate multiple learning styles through text at 6th-grade reading level, pictures, animations, and audio voiceovers. Functionalities include interactive educational modules on 11 OAMs and case-based patient stories on common barriers to OAM adherence.Early phase testing provided the opportunity to observe the user interface with the app and app functionality. Data were summarized descriptively from observations and comments of patient subjects.Thirty patient subjects provided input-19 in phase 1 usability testing and 11 in phase 2 feasibility testing. Comments provided by patient subjects during usability testing were largely positive. Responses included self-identification with patient stories, usefulness of drug information, preferences for text messages, and app limitations (e.g., perceived generational digital divide in technology use and potential patient inability to receive text messages). Using their feedback, modifications were made to the prototype app. Responses in feasibility testing demonstrated the app's usefulness across a wide range of ages. Highest opinion ratings on app usefulness were stated by patients who were newer to OAM therapy.User feedback suggests the potential benefit of the app as a tool to help patients with cancer, particularly after the first months for those starting new OAM regimens. Processes and lessons learned are transferable to other settings.

    View details for DOI 10.1016/j.japh.2018.12.014

    View details for Web of Science ID 000460662000017

    View details for PubMedID 30745188

  • Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification PLOS ONE Tcheng, D. K., Nayak, A. K., Fowlkes, C. C., Punyasena, S. W. 2016; 11 (2): e0148879


    Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based "pollen spotting" model to segment pollen grains from the slide background. We next tested ARLO's ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems.

    View details for DOI 10.1371/journal.pone.0148879

    View details for Web of Science ID 000370050700044

    View details for PubMedID 26867017

    View details for PubMedCentralID PMC4750970

  • Semi-automated segmentation of pollen grains in microscopic images: a tool for three imaging modes GRANA Johnsrud, S., Yang, H., Nayak, A., Punyasena, S. 2013; 52 (3): 181-191