Showing 1–13 of 13 results for author: Sehgal, N

Depression Symptoms and Relational Patterns in 187k ChatGPT Histories

Authors: Neil K. R. Sehgal, Dunigan Folk, Lyle Ungar, Sharath Chandra Guntuku

Abstract: Large language models are increasingly used as private, always-available conversational systems, but little is known about how people with depressive symptoms use them. Building on CSCW work on disclosure and peer support, we examine ChatGPT as an emerging informal support infrastructure: private, persistent, responsive, and available outside ordinary hours. We analyze 187,093 ChatGPT conversation… ▽ More Large language models are increasingly used as private, always-available conversational systems, but little is known about how people with depressive symptoms use them. Building on CSCW work on disclosure and peer support, we examine ChatGPT as an emerging informal support infrastructure: private, persistent, responsive, and available outside ordinary hours. We analyze 187,093 ChatGPT conversations from 766 participants who completed the PHQ-8, comparing those below the moderate-symptom threshold (score of 10) with those at or above it. Higher-PHQ participants used ChatGPT more for mental-health, interpersonal, loneliness, self-focused, and support-seeking conversations, with pronounced late-night and recurring month-level patterns. Their language contained more first-person singular pronouns and absolutist terms. They more often engaged ChatGPT in high-disclosure contexts, but professional redirection was not higher. Language-based prediction was modest and insufficient for screening (AUROC 0.591). We argue these histories should not be treated as clinical screening data but as evidence LLMs are increasingly used as informal support infrastructure. △ Less

Submitted 6 July, 2026; originally announced July 2026.

Journal ref: CSCW Companion '26: Companion Publication of the 2026 Conference on Computer-Supported Cooperative Work and Social Computing

Self-Reported Side Effects of Semaglutide and Tirzepatide in Online Communities

Authors: Neil K. R. Sehgal, Jena Shaw Tronieri, Lyle Ungar, Sharath Chandra Guntuku

Abstract: Social media can reveal patient experiences with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) that extend beyond clinical trial data. We analyzed 410,198 Reddit posts (May 2019-June 2025) mentioning semaglutide or tirzepatide. A total of 67,008 users self-reported using these medications, and 43.5% described at least one side effect. Gastrointestinal symptoms predominated, including nause… ▽ More Social media can reveal patient experiences with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) that extend beyond clinical trial data. We analyzed 410,198 Reddit posts (May 2019-June 2025) mentioning semaglutide or tirzepatide. A total of 67,008 users self-reported using these medications, and 43.5% described at least one side effect. Gastrointestinal symptoms predominated, including nausea (36.9%), fatigue (16.7%), vomiting (16.3%), constipation (15.3%), and diarrhea (12.6%). Notably, reproductive symptoms (e.g., menstrual irregularities) and temperature-related complaints (e.g., chills, hot flashes) emerged as unrecognized potential effects. These findings highlight patient concerns not well captured in current labeling or trials. Large-scale social media analysis can complement traditional pharmacovigilance by detecting emerging safety signals and expanding understanding of the real-world safety profile of GLP-1 RAs. △ Less

Submitted 12 March, 2026; originally announced March 2026.

Real-Time Deadlines Reveal Temporal Awareness Failures in LLM Strategic Dialogues

Authors: Neil K. R. Sehgal, Sharath Chandra Guntuku, Lyle Ungar

Abstract: Large Language Models (LLMs) generate text token-by-token in discrete time, yet real-world communication, from therapy sessions to business negotiations, critically depends on continuous time constraints. Current LLM architectures and evaluation protocols rarely test for temporal awareness under real-time deadlines. We use simulated negotiations between paired agents under strict deadlines to inve… ▽ More Large Language Models (LLMs) generate text token-by-token in discrete time, yet real-world communication, from therapy sessions to business negotiations, critically depends on continuous time constraints. Current LLM architectures and evaluation protocols rarely test for temporal awareness under real-time deadlines. We use simulated negotiations between paired agents under strict deadlines to investigate how LLMs adjust their behavior in time-sensitive settings. In a control condition, agents know only the global time limit. In a time-aware condition, they receive remaining-time updates at each turn. Deal closure rates are substantially higher (32\% vs. 4\% for GPT-5.1) and offer acceptances are sixfold higher in the time-aware condition than in the control, suggesting LLMs struggle to internally track elapsed time. However, the same LLMs achieve near-perfect deal closure rates ($\geq$95\%) under turn-based limits, revealing the failure is in temporal tracking rather than strategic reasoning. These effects replicate across negotiation scenarios and models, illustrating a systematic lack of LLM time awareness that will constrain LLM deployment in many time-sensitive applications. △ Less

Submitted 19 January, 2026; originally announced January 2026.

Designing Mental-Health Chatbots for Indian Adolescents: Mixed-Methods Evidence, a Boundary-Object Lens, and a Design-Tensions Framework

Authors: Neil K. R. Sehgal, Hita Kambhamettu, Sai Preethi Matam, Lyle Ungar, Sharath Chandra Guntuku

Abstract: Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. We report a mixed-methods study of Indian adolescents (survey n=362; interviews n=14) examining how they navigate mental-health challenges and engage with digital tools. Quantitative results highlight low self-stigma but significant s… ▽ More Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. We report a mixed-methods study of Indian adolescents (survey n=362; interviews n=14) examining how they navigate mental-health challenges and engage with digital tools. Quantitative results highlight low self-stigma but significant social stigma, a preference for text over voice interactions, and low utilization of mental health apps but high smartphone access. Our qualitative findings reveal that while adolescents value privacy, emotional support, and localized content in mental health tools, existing chatbots lack personalization and cultural relevance. We contribute (1) a Design-Tensions framework; (2) an artifact-level probe; and (3) a boundary-objects account that specifies how chatbots mediate adolescents, peers, families, and services. This work advances culturally sensitive chatbot design by centering on underrepresented populations, addressing critical gaps in accessibility and support for adolescents in India. △ Less

Submitted 10 November, 2025; originally announced November 2025.

Effect of Static vs. Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Controlled Trial

Authors: Neil K. R. Sehgal, Manuel Tonneau, Andy Tan, Shivan J. Mehta, Alison Buttenheim, Lyle Ungar, Anish K. Agarwal, Sharath Chandra Guntuku

Abstract: Large language model (LLM) chatbots show increasing promise in persuasive communication. Yet their real-world utility remains uncertain, particularly in clinical settings where sustained conversations are difficult to scale. In a pre-registered randomized controlled trial, we enrolled 915 U.S. adults (ages 45-75) who had never completed colorectal cancer (CRC) screening. Participants were randomiz… ▽ More Large language model (LLM) chatbots show increasing promise in persuasive communication. Yet their real-world utility remains uncertain, particularly in clinical settings where sustained conversations are difficult to scale. In a pre-registered randomized controlled trial, we enrolled 915 U.S. adults (ages 45-75) who had never completed colorectal cancer (CRC) screening. Participants were randomized to: (1) no message control, (2) expert-written patient materials, (3) single AI-generated message, or (4) a motivational interviewing chatbot. All participants were required to remain in their assigned condition for at least three minutes. Both AI arms tailored content using participant's self-reported demographics including age and gender. Both AI interventions significantly increased stool test intentions by over 12 points (12.9-13.8/100), compared to a 7.5 gain for expert materials (p<.001 for all comparisons). While the AI arms outperformed the no message control for colonoscopy intent, neither showed improvement xover expert materials. Notably, for both outcomes, the chatbot did not outperform the single AI message in boosting intent despite participants spending ~3.5 minutes more on average engaging with it. These findings suggest concise, demographically tailored AI messages may offer a more scalable and clinically viable path to health behavior change than more complex conversational agents and generic time intensive expert-written materials. Moreover, LLMs appear more persuasive for lesser-known and less-invasive screening approaches like stool testing, but may be less effective for entrenched preferences like colonoscopy. Future work should examine which facets of personalization drive behavior change, whether integrating structural supports can translate these modest intent gains into completed screenings, and which health behaviors are most responsive to AI-supported guidance. △ Less

Submitted 10 July, 2025; originally announced July 2025.

PAL: Designing Conversational Agents as Scalable, Cooperative Patient Simulators for Palliative-Care Training

Authors: Neil K. R. Sehgal, Hita Kambhamettu, Allen Chang, Andrew Zhu, Lyle Ungar, Sharath Chandra Guntuku

Abstract: Effective communication in serious illness and palliative care is essential but often under-taught due to limited access to training resources like standardized patients. We present PAL (Palliative Assisted Learning-bot), a conversational system that simulates emotionally nuanced patient interactions and delivers structured feedback grounded in an existing empathy-based framework. PAL supports tex… ▽ More Effective communication in serious illness and palliative care is essential but often under-taught due to limited access to training resources like standardized patients. We present PAL (Palliative Assisted Learning-bot), a conversational system that simulates emotionally nuanced patient interactions and delivers structured feedback grounded in an existing empathy-based framework. PAL supports text and voice modalities and is designed to scaffold clinical skill-building through repeated, low-cost practice. Through a mixed-methods study with 17 U.S. medical trainees and clinicians, we explore user engagement with PAL, evaluate usability, and examine design tensions around modalities, emotional realism, and feedback delivery. Participants found PAL helpful for reflection and skill refinement, though some noted limitations in emotional authenticity and the adaptability of feedback. We contribute: (1) empirical evidence that large language models can support palliative communication training; (2) design insights for modality-aware, emotionally sensitive simulation tools; and (3) implications for systems that support emotional labor, cooperative learning, and AI-augmented training in high-stakes care settings. △ Less

Submitted 2 July, 2025; originally announced July 2025.

Journal ref: CSCW Companion '25: Companion Publication of the 2025 Conference on Computer-Supported Cooperative Work and Social Computing

Large Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trial

Authors: Neil K. R. Sehgal, Sunny Rai, Manuel Tonneau, Anish K. Agarwal, Joseph Cappella, Melanie Kornides, Lyle Ungar, Alison Buttenheim, Sharath Chandra Guntuku

Abstract: Health care systems are increasingly considering large language model (LLM)-based chatbots for vaccine communication, but evidence that they improve durable, behaviorally relevant outcomes beyond existing health materials is limited. This randomized clinical trial tested whether brief, multiturn LLM chatbot interactions increased parental intention to vaccinate children against human papillomaviru… ▽ More Health care systems are increasingly considering large language model (LLM)-based chatbots for vaccine communication, but evidence that they improve durable, behaviorally relevant outcomes beyond existing health materials is limited. This randomized clinical trial tested whether brief, multiturn LLM chatbot interactions increased parental intention to vaccinate children against human papillomavirus (HPV) compared with no intervention and government public health materials, and whether effects persisted. Parents in the US, Canada, and UK were recruited online from March 3 to May 25, 2025, with follow-up at 15 and 45 days. Eligible participants were adults with at least one HPV vaccine-eligible child who was unvaccinated or whose vaccination status was unknown. Participants were randomized to no-message control, country-matched government materials with at least 3 minutes of exposure, or a 3-minute GPT-4o chatbot interaction using either a default persuasive style or a shorter conversational style. The primary outcome was self-reported likelihood of vaccinating the child against HPV within 12 months, measured immediately after intervention on a 0-100 scale. Follow-up outcomes included vaccination intent and self-reported vaccination at 15 and 45 days. In total, 1297 participants were randomized (mean age 42.84 years; 72.1% female). Compared with no intervention, public health materials increased immediate vaccination intent (Cohen d = 0.53; 95% CI, 0.36-0.70), as did the default chatbot (d = 0.48; 95% CI, 0.30-0.65) and conversational chatbot (d = 0.33; 95% CI, 0.17-0.49). At 45 days, neither chatbot increased intent relative to controls, whereas public health materials maintained modest effects. No intervention increased self-reported vaccination uptake. Findings suggest well-designed public health materials may match or exceed short LLM chatbot conversations for HPV vaccine promotion. △ Less

Submitted 9 June, 2026; v1 submitted 29 April, 2025; originally announced April 2025.

Journal ref: JAMA Network Open 2026

Exploring Socio-Cultural Challenges and Opportunities in Designing Mental Health Chatbots for Adolescents in India

Authors: Neil K. R. Sehgal, Hita Kambhamettu, Sai Preethi Matam, Lyle Ungar, Sharath Chandra Guntuku

Abstract: Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. Through a mixed-methods study involving a survey of 278 adolescents and follow-up interviews with 12 participants, we explore how adolescents perceive mental health challenges and interact with digital tools. Quantitative results high… ▽ More Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. Through a mixed-methods study involving a survey of 278 adolescents and follow-up interviews with 12 participants, we explore how adolescents perceive mental health challenges and interact with digital tools. Quantitative results highlight low self-stigma but significant social stigma, a preference for text over voice interactions, and low utilization of mental health apps but high smartphone access. Our qualitative findings reveal that while adolescents value privacy, emotional support, and localized content in mental health tools, existing chatbots lack personalization and cultural relevance. These findings inform recommendations for culturally sensitive chatbot design that prioritizes anonymity, tailored support, and localized resources to better meet the needs of adolescents in India. This work advances culturally sensitive chatbot design by centering underrepresented populations, addressing critical gaps in accessibility and support for adolescents in India. △ Less

Submitted 11 March, 2025; originally announced March 2025.

Journal ref: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems 2025

Race Discrimination in Internet Advertising: Evidence From a Field Experiment

Authors: Neil K. R. Sehgal, Dan Svirsky

Abstract: We present the results of an experiment documenting racial bias on Meta's Advertising Platform in Brazil and the United States. We find that darker skin complexions are penalized, leading to real economic consequences. For every \$1,000 an advertiser spends on ads with models with light-skin complexions, that advertiser would have to spend \$1,159 to achieve the same level of engagement using phot… ▽ More We present the results of an experiment documenting racial bias on Meta's Advertising Platform in Brazil and the United States. We find that darker skin complexions are penalized, leading to real economic consequences. For every \$1,000 an advertiser spends on ads with models with light-skin complexions, that advertiser would have to spend \$1,159 to achieve the same level of engagement using photos of darker skin complexion models. Meta's budget optimization tool reinforces these viewer biases. When pictures of models with light and dark complexions are allocated a shared budget, Meta funnels roughly 64\% of the budget towards photos featuring lighter skin complexions. △ Less

Submitted 18 December, 2024; originally announced December 2024.

Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data

Authors: Shuangjun Liu, Naveen Sehgal, Sarah Ostadabbas

Abstract: The ultimate goal for an inference model is to be robust and functional in real life applications. However, training vs. test data domain gaps often negatively affect model performance. This issue is especially critical for the monocular 3D human pose estimation problem, in which 3D human data is often collected in a controlled lab setting. In this paper, we focus on alleviating the negative effec… ▽ More The ultimate goal for an inference model is to be robust and functional in real life applications. However, training vs. test data domain gaps often negatively affect model performance. This issue is especially critical for the monocular 3D human pose estimation problem, in which 3D human data is often collected in a controlled lab setting. In this paper, we focus on alleviating the negative effect of domain shift in both appearance and pose space for 3D human pose estimation by presenting our adapted human pose (AHuP) approach. AHuP is built upon two key components: (1) semantically aware adaptation (SAA) for the cross-domain feature space adaptation, and (2) skeletal pose adaptation (SPA) for the pose space adaptation which takes only limited information from the target domain. By using zero real 3D human pose data, one of our adapted synthetic models shows comparable performance with the SOTA pose estimation models trained with large scale real 3D human datasets. The proposed SPA can be also employed independently as a light-weighted head to improve existing SOTA models in a novel context. A new 3D scan-based synthetic human dataset called ScanAva+ is also going to be publicly released with this work. △ Less

Submitted 22 January, 2022; v1 submitted 22 May, 2021; originally announced May 2021.

Optimising Design Verification Using Machine Learning: An Open Source Solution

Authors: B. Samhita Varambally, Naman Sehgal

Abstract: With the complexity of Integrated Circuits increasing, design verification has become the most time consuming part of the ASIC design flow. Nearly 70% of the SoC design cycle is consumed by verification. The most commonly used approach to test all corner cases is through the use of Constrained Random Verification. Random stimulus is given in order to hit all possible combinations and test the desi… ▽ More With the complexity of Integrated Circuits increasing, design verification has become the most time consuming part of the ASIC design flow. Nearly 70% of the SoC design cycle is consumed by verification. The most commonly used approach to test all corner cases is through the use of Constrained Random Verification. Random stimulus is given in order to hit all possible combinations and test the design thoroughly. However, this approach often requires significant human expertise to reach all corner cases. This paper presents an alternative using Machine Learning to generate the input stimulus. This will allow for faster thorough verification of the design with less human intervention. Furthermore, it is proposed to use the open source verification environment 'Cocotb'. Based on Python, it is simple, intuitive and has a vast library of functions for machine learning applications. This makes it more convenient to use than the bulkier approach using traditional Hardware Verification Languages such as System Verilog or Specman E. △ Less

Submitted 4 December, 2020; originally announced December 2020.

Can an Algorithm be My Healthcare Proxy?

Authors: Duncan C McElfresh, Samuel Dooley, Yuan Cui, Kendra Griesman, Weiqin Wang, Tyler Will, Neil Sehgal, John P Dickerson

Abstract: Planning for death is not a process in which everyone participates. Yet a lack of planning can have vast impacts on a patient's well-being, the well-being of her family, and the medical community as a whole. Advance Care Planning (ACP) has been a field in the United States for a half-century. Many modern techniques prompting patients to think about end of life (EOL) involve short surveys or questi… ▽ More Planning for death is not a process in which everyone participates. Yet a lack of planning can have vast impacts on a patient's well-being, the well-being of her family, and the medical community as a whole. Advance Care Planning (ACP) has been a field in the United States for a half-century. Many modern techniques prompting patients to think about end of life (EOL) involve short surveys or questionnaires. Different surveys are targeted to different populations (based off of likely disease progression or cultural factors, for instance), are designed with different intentions, and are administered in different ways. There has been recent work using technology to increase the number of people using advance care planning tools. However, modern techniques from machine learning and artificial intelligence could be employed to make additional changes to the current ACP process. In this paper we will discuss some possible ways in which these tools could be applied. We will discuss possible implications of these applications through vignettes of patient scenarios. We hope that this paper will encourage thought about appropriate applications of artificial intelligence in ACP as well as implementation of AI in order to ensure intentions are honored. △ Less

Submitted 7 January, 2020; originally announced January 2020.

Comments: Accepted for a poster presentation at the 4th International Workshop on Health Intelligence (W3PHIAI-20), colocated with AAAI 2020

Efficient FPGA Implementation of Conjugate Gradient Methods for Laplacian System using HLS

Authors: Sahithi Rampalli, Natasha Sehgal, Ishita Bindlish, Tanya Tyagi, Pawan Kumar

Abstract: In this paper, we study FPGA based pipelined and superscalar design of two variants of conjugate gradient methods for solving Laplacian equation on a discrete grid; the first version corresponds to the original conjugate gradient algorithm, and the second version corresponds to a slightly modified version of the same. In conjugate gradient method to solve partial differential equations, matrix v… ▽ More In this paper, we study FPGA based pipelined and superscalar design of two variants of conjugate gradient methods for solving Laplacian equation on a discrete grid; the first version corresponds to the original conjugate gradient algorithm, and the second version corresponds to a slightly modified version of the same. In conjugate gradient method to solve partial differential equations, matrix vector operations are required in each iteration; these operations can be implemented as 5 point stencil operations on the grid without explicitely constructing the matrix. We show that a pipelined and superscalar design using high level synthesis written in C language leads to a significant reduction in latencies for both methods. When comparing these two, we show that the later has roughly two times lower latency than the former given the same degree of superscalarity. These reductions in latencies for the newer variant of CG is due to parallel implementations of stencil operation on subdomains of the grid, and dut to overlap of these stencil operations with dot product operations. In a superscalar design, domain needs to be partitioned, and boundary data needs to be copied, which requires padding. In 1D partition, the padding latency increases as the number of partitions increase. For a streaming data flow model, we propose a novel traversal of the grid for 2D domain decomposition that leads to 2 times reduction in latency cost involved with padding compared to 1D partitions. Our implementation is roughly 10 times faster than software implementation for linear system of dimension $10000 \times 10000.$ △ Less

Submitted 10 March, 2018; originally announced March 2018.

Comments: 10 pages, 11 figures, 5 tables