2 citations · 2 across the 13 of their papers we have counts for
13 papers
Clinician-Grounded Quality Assurance for AI-Assisted Psychiatric Intake
King Shi, Amanda Li, Jonathan Ivey +7
Before patients can use AI-assisted psychiatric intake systems, health systems need practical ways to routinely evaluate these tools against their clinical standards for quality as…
Reliability Auditing for Downstream LLM tasks in Psychiatry: LLM-Generated Hospitalization Risk Scores
Shevya Panda, Shinjini Bose, Ananya Joshi
Large language models (LLMs) are increasingly utilized in clinical reasoning and risk assessment. However, their interpretive reliability in critical and indeterminate domains such…
Context-Aware Hospitalization Forecasting Evaluations for Decision Support using LLMs
Rhea Makkuni, Ananya Joshi
Medical and public health experts must make real-time resource decisions, such as expanding hospital bed capacity, based on projected hospitalization trends during large-scale heal…
Optimal Question Selection from a Large Question Bank for Clinical Field Recovery in Conversational Psychiatric Intake
Guan Gui, Peter Zandi, Jacob Taylor +1
Psychiatric intake is a sequential, high-stakes information-gathering process in which clinicians must decide what to ask, in what order, and how to interpret incomplete or ambiguo…
Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems
Meghana Karnam, Ananya Joshi
Emerging AI systems in behavioral health and psychiatry use multi-step or multi-agent LLM pipelines for tasks like assessing self-harm risk and screening for depression. However, c…
Constrained Process Maps for Multi-Agent Generative AI Workflows
Ananya Joshi, Michael Rudow
Large language model (LLM)-based agents are increasingly used to perform complex, multi-step workflows in regulated settings such as compliance and due diligence. However, many age…
Decomposing Theory of Mind: How Emotional Processing Mediates ToM Abilities in LLMs
Ivan Chulo, Ananya Joshi
Recent work shows activation steering substantially improves language models' Theory of Mind (ToM) (Bortoletto et al. 2024), yet the mechanisms of what changes occur internally tha…
Predicting Language Models' Success at Zero-Shot Probabilistic Prediction
Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño +7
Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics (e.g., to serve as risk models or…
Calibrating Lightweight Sparse Autoencoder Feature Steering
Ananya Joshi, Celia Cintas, Skyler Speakman
Sparse autoencoders (SAEs) can enable inference-time topic steering by modifying latent feature activations, but existing steering methods often fail when target-aligned features a…
Inhibiting Alzheimer's Disease by Targeting Aggregation of Beta-Amyloid
Ananya Joshi, George Khoury, Christodoulas Floudas
Alzheimer's disease is characterized by dangerous amyloid plaques formed by deposits of the protein Beta-Amyloid aggregates in the brain. The specific amino acid sequence that is r…
An AI-Based Public Health Data Monitoring System
Ananya Joshi, Nolan Gormley, Richa Gadgil +3
Public health experts need scalable approaches to monitor large volumes of health data (e.g., cases, hospitalizations, deaths) for outbreaks or data quality issues. Traditional ale…
Outlier Ranking in Large-Scale Public Health Streams
Ananya Joshi, Tina Townes, Nolan Gormley +3
Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, th…
Computationally Assisted Quality Control for Public Health Data Streams
Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld +1
Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the mo…