ArticleJournal of biomedical informatics2024
SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization.
Article in Journal of biomedical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review.International journal of medical informatics · 2026Pooled it
- Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Ophthalmology Continuing Medical Education Questions.Ophthalmology science · 2026Article
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Article
- Scientific Evidence for Clinical Text Summarization Using Large Language Models: Scoping Review.Journal of medical Internet research · 2025Article
- Machine Learning Models as Early Warning Systems for Neonatal Infection.Clinics in perinatology · 2025Review
- Application of large language models in clinical record correction: a comprehensive study on various retraining methods.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models.BJR artificial intelligence · 2025Review
- "Hospice Care Could Be a Compassionate Choice": ChatGPT Responses to Questions About Decision Making in Advanced Cancer.Journal of palliative medicine · 2024Article
- A pilot feasibility study comparing large language models in extracting key information from ICU patient text records from an Irish population.Intensive care medicine experimental · 2024Article
- Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.Cancer discovery · 2024Review
- Prospects for AI clinical summarization to reduce the burden of patient chart review.Frontiers in digital health · 2024Review
- Enhancing Long-Term Care Efficiency: Embedded LLMs for Clinical Report Summarization and Caregiver Support.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating instruction prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased performance variance, resulting in significantly distinct summaries even when instruction prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-BasedCalibration (SPeC) pipeline that employs soft prompts to lower variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively regulates variance across different LLMs, providing a more consistent and reliable approach to summarizing critical medical information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.