Year 3 Pilots

Publication: An Explainable Transformer Model for Pain Intensity Assessment Using Multi-Modal Facial Sequential Images

Authors: Xian Du, Meysam Safarzadeh, Maoqin Zhu, Shishir Prasad, Sudeshna Das, Joohyun Chung Abstract Pain monitoring and assessment traditionally rely on subjective methods such as self-reports and caregiver evaluations, which can be costly and often inaccurate due to their inherent subjectivity and reliance on the individual's communication skills. Many objective methods have been introduced to address these issues, primarily utilizing single or multiple wearable sensor modalities. However, these approaches face challenges in home care settings, particularly concerning continuous wearability and discomfort, especially among elderly users. An alternative solution is using patient monitoring tools such as various imaging modalities to detect pain-related facial expressions. In this paper, we developed a new transformer model to extract pain-related features from facial expressions captured through three imaging modalities—RGB, thermal, and depth across sequential images. This method can leverage the…

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Oral Presentation: Old School Meets New School: Voice-Based, AI-Enabled Cognitive Rehabilitation for Dementia Care

This is part of the monthly MassAITC webinar series. Abstract: Cognitive rehabilitation therapy supports individuals living with mild cognitive impairment and early-stage dementia in maintaining and improving function in daily life. However, access remains limited due to constraints in the availability and scalability of trained therapists. Recent advances in artificial intelligence, combined with evolving reimbursement pathways for remote care in the United States, now make virtual delivery models increasingly viable, creating new opportunities to expand access to high-quality cognitive care. Moneta Health has developed a telephone-based cognitive rehabilitation platform that enables structured, personalized therapy sessions delivered remotely and overseen by licensed speech-language pathologists. The platform leverages AI-driven speech analysis and automated session orchestration to support consistent therapy delivery while preserving clinician oversight, enabling older adults to engage in care from their homes through a familiar…

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Publication: Replicable Bandits for Digital Health Interventions

Authors: Kelly W Zhang, Nowell Closser, Anna L Trella, Susan A Murphy Abstract Adaptive treatment assignment algorithms, such as bandit algorithms, are increasingly used in digital health intervention clinical trials. Frequently the data collected from these trials is used to conduct causal inference and related data analyses to decide how to refine the intervention, and whether to roll-out the intervention more broadly. This work studies inference for estimands that depend on the adaptive algorithm itself; a simple example is the mean reward under the adaptive algorithm. Specifically, we investigate the replicability of statistical analyses concerning such estimands when using data from trials deploying adaptive treatment assignment algorithms. We demonstrate that many standard statistical estimators can be inconsistent and fail to be replicable across repetitions of the clinical trial, even as the sample size grows large.…

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Oral Presentation: Can You Walk Me Through It? Explainable SMS Phishing Detection using LLM-based Agents

This is part of the monthly MassAITC webinar series. Abstract: Phishing attacks pose a significant threat to users, especially older adults. Existing defenses mainly focus on phishing detection but often cannot explain to lay users why a message is malicious. In this talk, I will discuss how we use Large Language Models (LLMs) to detect SMS phishing while generating evidence-based explanations. The key challenge is that SMS is short, lacking the necessary context for security reasoning. We develop a prototype called SmishX that gathers external contexts to augment the chain-of-thought (CoT) reasoning of LLMs and facilitate the explanation process. I will further discuss our user studies to evaluate the effectiveness and usability of SmishX. Finally, I will discuss the open challenges and opportunities of using AI to help older adults better protect themselves from cybersecurity…

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Oral Presentation: American Speech Language Hearing Association Conference – 2025

SLPs on the Line: Engaging Patients in Cognitive Rehabilitation via Telephone: Moneta Health has developed a telephone-based telepractice model to deliver cognitive rehabilitation therapy (CR) to older adults with cognitive impairment. This model was designed to address common barriers to accessing quality care through traditional in-clinic and telehealth services. With Moneta, patients receive telephone sessions delivered by speech-language pathologists (SLPs), and sessions delivered by an AI-powered automated agent. These automated sessions contain personalized, interactive cognitive activities designed and selected by an SLP. An analysis of over 100 patients who completed the program shows high engagement, compliance and satisfaction, supporting the use of digital and audio-only delivery to promote a positive patient experience. In this paper, we provide an overview of the patient experience with Moneta, and review engagement and satisfaction metrics. Source: https://plan.core-apps.com/asha2025/event/28734884ce4484665e296583a03904a0

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Award: Best Demo Audience Choice Award at IEEE BSN 2025

The team of Colin Barry, Tatsuo Kumamoto, Edward Wang, and Lina Battikha won the Audience Choice Award for Best Demo at the IEEE-EMBS International Conference on Body Sensor Networks – Computational Medicine: Expanding Health through Sensing and AI held from November 3-5, 2025 for their demo entitled, "Oscillometric Smartphone Blood Pressure Demo." Source: LinkedIn Post

Continue ReadingAward: Best Demo Audience Choice Award at IEEE BSN 2025