ArticleScientific data2023
Aci-bench: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.JAMA · 2025Pooled it
- Automated Brief Hospital Course Summarization in Cardiac Surgery Using a Lightweight Large Language Model-Based Framework: Development and Evaluation Study on the Medical Information Mart for Intensive Care-IV.Journal of medical Internet research · 2026Article
- Readability of AI-Generated Patient Visit Summaries in Orthopedic Surgery: Retrospective Analysis.Journal of medical Internet research · 2026Article
- What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Automated evaluation can distinguish the good and bad AI responses to patient questions about hospitalization.NPJ digital medicine · 2026Article
- The Aloe Family recipe for open and specialized healthcare LLMs.NPJ digital medicine · 2026Article
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- Holistic evaluation of large language models for medical tasks with MedHELM.Nature medicine · 2026Article
- A Dataset for Addressing Patient's Information Needs related to Clinical Course of Hospitalization.Scientific data · 2026Article
- Evaluating privacy leakages in LLM-driven ambient clinical documentation.Frontiers in digital health · 2026Article
- An Empirical Evaluation of Low-Rank Adapted Vision-Language Models for Radiology Image Captioning.Bioengineering (Basel, Switzerland) · 2025Article
- AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration.JMIR medical informatics · 2025Article
- A perspective for adapting generalist AI to specialized medical AI applications and their challenges.NPJ digital medicine · 2025Review
- Ambient technology in epilepsy clinical practice.Epilepsia open · 2025Article
- Impact of acoustic and informational noise on AI-generated clinical summaries.BMJ digital health & AI · 2025Article
- Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice.BMJ digital health & AI · 2025Article
- Artificial intelligence-generated feedback on social signals in patient-provider communication: technical performance, feedback usability, and impact.JAMIA open · 2024Article
- CACER: Clinical concept Annotations for Cancer Events and Relations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Exploring the Feasibility of GPT-4 as a Data Extraction Tool for Renal Surgery Operative Notes.Urology practice · 2024Article
- NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes.Findings of ACL. ACL · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent immense breakthroughs in generative models such as in GPT4 have precipitated re-imagined ubiquitous usage of these models in all applications. One area that can benefit by improvements in artificial intelligence (AI) is healthcare. The note generation task from doctor-patient encounters, and its associated electronic medical record documentation, is one of the most arduous time-consuming tasks for physicians. It is also a natural prime potential beneficiary to advances in generative models. However with such advances, benchmarking is more critical than ever. Whether studying model weaknesses or developing new evaluation metrics, shared open datasets are an imperative part of understanding the current state-of-the-art. Unfortunately as clinic encounter conversations are not routinely recorded and are difficult to ethically share due to patient confidentiality, there are no sufficiently large clinic dialogue-note datasets to benchmark this task. Here we present the Ambient Clinical Intelligence Benchmark (ACI-BENCH) corpus, the largest dataset to date tackling the problem of AI-assisted note generation from visit dialogue. We also present the benchmark performances of several common state-of-the-art approaches.
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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.