Abhay Goyal

Tran Hien Van, Abhay Goyal, Muhammad Siddique et al.

The proliferation of discussion about fatherhood in Singapore attests to its significance, indicating the need for an exploration of how fatherhood is framed, aiding policy-making around fatherhood in Singapore. Sound and holistic policy around fatherhood in Singapore may reduce stigma and apprehension around being a parent, critical to improving the nations flagging birth rate. We analyzed 15,705 articles and 56,221 posts to study how fatherhood is framed in Singapore across a range of online platforms (news outlets, parenting forums, Twitter). We used NLP techniques to understand these differences. While fatherhood was framed in a range of ways on the Singaporean online environment, it did not seem that fathers were framed as central to the Singaporean family unit. A strength of our work is how the different techniques we have applied validate each other.

Abhay Goyal, Muhammad Siddique, Nimay Parekh et al.

Recent developments in natural language processing have demonstrated the potential of large language models (LLMs) to improve a range of educational and learning outcomes. Of recent chatbots based on LLMs, ChatGPT and Bard have made it clear that artificial intelligence (AI) technology will have significant implications on the way we obtain and search for information. However, these tools sometimes produce text that is convincing, but often incorrect, known as hallucinations. As such, their use can distort scientific facts and spread misinformation. To counter polarizing responses on these tools, it is critical to provide an overview of such responses so stakeholders can determine which topics tend to produce more contentious responses -- key to developing targeted regulatory policy and interventions. In addition, there currently exists no annotated dataset of ChatGPT and Bard responses around possibly polarizing topics, central to the above aims. We address the indicated issues through the following contribution: Focusing on highly polarizing topics in the US, we created and described a dataset of ChatGPT and Bard responses. Broadly, our results indicated a left-leaning bias for both ChatGPT and Bard, with Bard more likely to provide responses around polarizing topics. Bard seemed to have fewer guardrails around controversial topics, and appeared more willing to provide comprehensive, and somewhat human-like responses. Bard may thus be more likely abused by malicious actors. Stakeholders may utilize our findings to mitigate misinformative and/or polarizing responses from LLMs

Abhay Goyal, Navin Kumar, Kimberly DiMeola et al.

Chronic pain (CP) and opioid use disorder (OUD) are common and interrelated chronic medical conditions. Currently, there is a paucity of evidence-based integrated treatments for CP and OUD among individuals receiving medication for opioid use disorder (MOUD). Wearable devices have the potential to monitor complex patient information and inform treatment development for persons with OUD and CP, including pain variability (e.g., exacerbations of pain or pain spikes) and clinical correlates (e.g., perceived stress). However, the application of large language models (LLMs) with wearable data for understanding pain spikes, remains unexplored. Consequently, the aim of this pilot study was to examine the clinical correlates of pain spikes using a range of AI approaches. We found that machine learning models achieved relatively high accuracy (>0.7) in predicting pain spikes, while LLMs were limited in providing insights on pain spikes. Real-time monitoring through wearable devices, combined with advanced AI models, could facilitate early detection of pain spikes and support personalized interventions that may help mitigate the risk of opioid relapse, improve adherence to MOUD, and enhance the integration of CP and OUD care. Given overall limited LLM performance, these findings highlight the need to develop LLMs which can provide actionable insights in the OUD/CP context.

Shayan Vassef, Soorya Ram Shimegekar, Abhay Goyal et al.

Clinical ML workflows are often fragmented and inefficient: triage, task selection, and model deployment are handled by a patchwork of task-specific networks. These pipelines are rarely aligned with data-science practice, reducing efficiency and increasing operational cost. They also lack data-driven model identification (from imaging/tabular inputs) and standardized delivery of model outputs. We present a framework that employs a single vision-language model (VLM) in two complementary, modular roles. First (Solution 1): the VLM acts as an aware model-card matcher that routes an incoming image to the appropriate specialist model via a three-stage workflow (modality -> primary abnormality -> model-card ID). Reliability is improved by (i) stage-wise prompts enabling early termination via "None"/"Other" and (ii) a calibrated top-2 answer selector with a stage-wise cutoff. This raises routing accuracy by +9 and +11 percentage points on the training and held-out splits, respectively, compared with a baseline router, and improves held-out calibration (lower Expected Calibration Error, ECE). Second (Solution 2): we fine-tune the same VLM on specialty-specific datasets so that one model per specialty covers multiple downstream tasks, simplifying deployment while maintaining performance. Across gastroenterology, hematology, ophthalmology, pathology, and radiology, this single-model deployment matches or approaches specialized baselines. Together, these solutions reduce data-science effort through more accurate selection, simplify monitoring and maintenance by consolidating task-specific models, and increase transparency via per-stage justifications and calibrated thresholds. Each solution stands alone, and in combination they offer a practical, modular path from triage to deployment.