Course: Large Language Models for Healthcare Applications
Department of Digital Medical Technologies, Holon Institute of Technology
2025 Spring
Lecturer: Dr. Alexander(Sasha) Apartsin
HoS Course Series Home: Here
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StudenT Projects
StudenT Projects
MedQDx
MedQDx
GenAI-Powered Learning for Sharper Clinical Reasoning
Mai Werthaim, Maya Kimhi
The diagnostic process is inherently interactive, often driven by a clinician’s ability to ask the right questions in response to incomplete or ambiguous patient information. However, current diagnostic benchmarks for large language models (LLMs) typically rely on fully disclosed patient cases, overlooking the critical role of iterative inquiry and adaptive reasoning. In this work, we introduce Q4Dx, a novel benchmark designed to evaluate the capacity of large language models (LLMs) to perform interactive diagnosis through patient interrogation.
PatientSignal
PatientSignal
Diagnosing Through the Noise: Understanding Patient Self-Descriptions
Liel Sheri, Eden Mama
PatientSignal is a project that examines how well different NLP models can classify patient-reported symptoms when the input is messy, unclear, or written in everyday language rather than formal clinical terms. To simulate real-world communication, we took clean symptom descriptions. We used the LLaMA 3.1 model to add realistic noise, such as hesitations, repeated phrases, and off-topic comments, mimicking how elderly patients or those under stress might describe their condition. We created three versions of the dataset: clean, medium noise, and heavy noise, and used them to train and evaluate several models.
MediGuard
MediGuard
Monitoring Medication Questions to Detect Emerging Safety Concerns
Dvora Goncharok , Arbel Shifman
Online medical forums are a rich and underutilized source of insight into patient concerns, especially regarding medication use. Some of the many questions users pose may signal confusion, misuse, or even the early warning signs of a developing health crisis. Detecting these critical questions, those that may precede severe adverse events or life-threatening complications, is vital for timely intervention and improving patient safety. This study introduces a novel dataset of medication-related questions, annotated from online forums.
ADR Detect
ADR Detect
Detection of Adverse Drug Reactions in Clinical Sentences
Naveh Nissan, Nicole Poliak
Identifying adverse drug reactions (ADRs) in clinical narratives is a critical task in medical natural language processing, supporting pharmacovigilance and patient safety efforts. This study focuses on sentence-level ADR classification and compares three methodological paradigms: traditional TF-IDF features with logistic regression, contextual embeddings with Sentence-BERT (SBERT), and generative large language models (LLMs) for zero-shot and few-shot classification.
VibeWatch
VibeWatch
Listening to Social Signals for Mental Health Insights
Dudi Saadia, Shahar Sadon, Shanel Asulin
The rise of social media as a dominant form of online expression has created new opportunities for identifying indicators of mental health conditions such as depression and post-traumatic stress disorder (PTSD) through textual analysis. This study investigates the application of generative large language models (LLMs) for detecting signs of psychological distress in user-generated posts and comments.
AutoImpress
AutoImpress
From Findings to Insight: Automated Radiology Impressions
Netanel Ohev Shalom, Yaniv Grosberg, Aviel Shmuel
Radiology impressions provide essential diagnostic summaries from detailed report findings and are integral to clinical workflows. Automating impression generation holds promise for reducing radiologist workload and improving report consistency. In this study, we explore the use of medical language models to generate impressions from the findings sections of radiology reports. We use domain-adapted transformer architectures to compare three learning paradigms—zero-shot prompting, few-shot prompting, and full fine-tuning.
LangTriage
LangTriage
Analysing ER Complaints with AI to Prioritize Critical Cases Faster
Nofar Kedmi, Diana Akoshvili
Triage is a time-critical process that determines the urgency of patient care, often based on real-time verbal descriptions from patients or clinical staff. Frequently derived from speech and transcribed into free text, these descriptions form the basis for assessing whether immediate medical intervention is required. LangTriage is an NLP-based system designed to classify patient cases into urgency levels, such as urgent or non-urgent, based on natural language descriptions.
MedTruth
MedTruth
Your AI shield against health misinformation
Sara Mangistu, Michelle Zalevsky
The proliferation of medical misinformation on social media presents a growing threat to public health, particularly during global health emergencies. This study investigates the use of large language models (LLMs) to detect and classify medical misinformation in user-generated content. We frame the task as a claim verification problem, leveraging real-world datasets such as COVID19-Fake-News, PubHealth, and HealthLiesRate (HLR) to train and evaluate LLM-based classifiers.
PharmAFeel
PharmAFeel
AI-driven sentiment analysis to understand how patients truly feel about their medications
Nikol Jabotinski, Yuval Elisha
PharmaFeel is a Natural Language Processing (NLP) project focused on analyzing unstructured patient reviews related to medications to extract sentiment based on patient experiences. Each review is classified as positive, neutral, or negative to capture public perception and satisfaction with specific treatments. The project compares traditional machine learning approaches with modern large language model (LLM)-based techniques to evaluate their effectiveness in handling informal, subjective, and often noisy patient-generated content.
Virtual Care Assistant
Virtual Care Assistant
Tracking What Matters: Detecting Changes in Patient Narratives
Gabrielle Maor, Shay Sason
Timely detection of significant changes in patient status is critical in home care settings, where early intervention can prevent deterioration and reduce hospital readmissions. This study presents an approach for identifying clinically meaningful status changes by combining caregiver-provided textual descriptions with structured vital sign measurements. We leverage large language models (LLMs) to analyze and interpret natural language reports, often informal and context-rich, in conjunction with physiological data to determine whether a significant change has occurred.
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