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Dr. Sarah Chen
Stanford · NLP & interpretability
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Fairness + NLP projects overlap with her recent work.
Strong methods fit: model evaluation and interpretability.
Clear outreach angle for current trustworthy AI research.
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Ask how her lab is extending interpretability methods into fairness audits for real-world AI systems.
Kavita Sarin is an Associate Professor of Dermatology at Stanford University and is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). Her work focuses on the application of artificial intelligence in the field of medicine and imaging, contributing to the advancement of healthcare through innovative AI-driven solutions. She is involved in research initiatives that aim to improve diagnostic accuracy and treatment outcomes by leveraging AI technologies in dermatology and related medical disciplines.
Abstracts: Background: Pediatric atopic dermatitis (AD) can pose a significant financial burden to families. However, no studies exist that assess the impact of pediatric AD on health care access/affordability at the parental level. Objective: Explore the effects of childhood AD on parental access to health care and the socioeconomic factors that might exacerbate these problems. Methods: The National Health Interview Survey was used to analyze 48,329,314 participants who answered the validated question on pediatric AD. Multivariable logistic regression analyses were performed to assess the association between pediatric AD and parental access to care. Results: Parents of children with AD were more likely to have difficulty accessing prescription medications (aOR: 1.47; [95% CI 1.31–1.65]), follow-up care (1.36; [95% CI 1.17–1.57]), specialist care (aOR: 1.53; [95% CI 1.33–1.75]), and more likely to purchase medications from abroad (aOR: 1.35; [95% CI 1.09–1.67]) relative to their counterparts with children without AD. Within the AD cohort, uninsured or lower income participants had higher odds of facing these barriers to care. Conclusions: Parents of children with AD are more likely to face barriers in health care access, and significant disparities exist based on sociodemographic characteristics.
Importance: Patient-centered research is essential for bridging the gap between research and patient care, yet patient perspectives are often inadequately represented in health research. Objective: To leverage artificial intelligence (AI) and natural language processing (NLP) to analyze a large dataset of patient messages, defining patient concerns and generating relevant research topics, and to quantify the quality of these AI-generated topics. Design, Setting, and Participants: This case series was conducted using an automated framework involving a 2-staged unsupervised NLP topic model and AI-generated research topic suggestions. The study was based on deidentified patient portal message data from individuals with breast or skin cancer at Stanford Health Care and 22 affiliated centers over July 2013 to April 2024. Exposures: A widely used large language model (ChatGPT-4o [OpenAI]; April 2024) was used and guided through multiple prompt-engineering strategies to perform multilevel tasks, including knowledge interpretation and summarization (eg, interpreting and summarizing the NLP-defined topics), knowledge generation (eg, generating research ideas corresponding to patients' issues), self-reflection and correction (eg, ensuring and revising the research ideas after searching for scientific articles), and self-reassurance (eg, confirming and finalizing the research ideas). Main Outcomes and Measures: Three breast oncologists (J.L.C., A.W.K., F.R) and 3 dermatologists (K.Y.S, J.Y.T., E.L.) evaluated the meaningfulness and novelty of the AI-generated research topics using a 5-point Likert scale (1 representing exceptional to 5 representing poor). Mean (SD) scores for meaningfulness and novelty were computed for each topic. Results: A total of 614 464 patient messages were analyzed from 25 549 individuals, 10 665 with breast cancer (98.6% female) and 14 884 had skin cancer (49.0% female). The overall mean (SD) scores for meaningfulness and novelty were 3.00 (0.50) and 3.29 (0.74), respectively, for breast cancer topics and 2.67 (0.45) and 3.09 (0.68), respectively, for skin cancer topics. One-third of the AI-suggested research topics were highly meaningful and novel when both scores were lower than the average (5 of 15 for breast cancer and 6 of 15 for skin cancer). Notably, two-thirds of the AI-suggested topics were novel (10 of 15 for breast cancer and 11 of 15 for skin cancer). Conclusions and Relevance: This case series demonstrates that AI/NLP-driven analysis of large volumes of patient messages can generate quality research topics in cancer care that reflect patient perspectives, providing valuable guidance for future patient-centered health research endeavors.
Basal cell carcinoma (BCC), the most common human cancer, is driven by hyperactivation of the Hedgehog pathway mediated by Smoothened (SMO) signaling and Glioma-Associated Oncogene Homolog (GLI) transcription. Gαs and protein kinase A (PKA) negatively regulate Hedgehog signaling, offering a potential alternative BCC development and treatment pathway. In this study, using histology alongside bulk and single-cell RNA sequencing, we found that mouse BCC-like tumors that originate from Gαs pathway inactivation are highly similar to those driven by canonical Hedgehog signaling induced by constitutive SMO activation. Both pathways led to the expansion of basal stem cells in the skin, with tumor cells clustering in two distinct populations with markers for touch dome and isthmus stem cell-like cells. Interestingly, mutations that reduce Gαs and PKA activity were present in human BCC. Tumors from Gαs pathway inactivation were independent of the canonical Hedgehog regulators SMO and GPR161, establishing them as SMO-independent oncogenic Hedgehog signaling models. Finally, activation of the Gαs-coupled adenosine 2B receptor with BAY60-6583 counteracted oncogenic SMO, reducing Hedgehog signaling and tumor growth. Together, these findings offer a potential therapeutic strategy for BCC. SIGNIFICANCE: Gαs/PKA pathway inactivation drives Hedgehog-dependent basal cell carcinoma and can be counteracted by activation of the Gαs-coupled adenosine 2B receptor to suppress tumor growth, providing a potential treatment for Hedgehog-driven tumors.
