Intelligent Documentation in Medical Education: Can AI Replace Manual Case Logging?

• URL: http://arxiv.org/abs/2601.12648v1
• Date: Mon, 19 Jan 2026 01:45:51 GMT
• Title: Intelligent Documentation in Medical Education: Can AI Replace Manual Case Logging?
• Authors: Nafiz Imtiaz Khan, Kylie Cleland, Vladimir Filkov, Roger Eric Goldman,
• Abstract summary: Procedural case logs are a core requirement in radiology training, yet they are time-consuming to complete and prone to inconsistency when authored manually.<n>This study investigates whether large language models (LLMs) can automate procedural case log documentation directly from free-text radiology reports.
• Score: 1.3266402517619373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Procedural case logs are a core requirement in radiology training, yet they are time-consuming to complete and prone to inconsistency when authored manually. This study investigates whether large language models (LLMs) can automate procedural case log documentation directly from free-text radiology reports. We evaluate multiple local and commercial LLMs under instruction-based and chain-of-thought prompting to extract structured procedural information from 414 curated interventional radiology reports authored by nine residents between 2018 and 2024. Model performance is assessed using sensitivity, specificity, and F1-score, alongside inference latency and token efficiency to estimate operational cost. Results show that both local and commercial models achieve strong extraction performance, with best F1-scores approaching 0.87, while exhibiting different trade-offs between speed and cost. Automation using LLMs has the potential to substantially reduce clerical burden for trainees and improve consistency in case logging. These findings demonstrate the feasibility of AI-assisted documentation in medical education and highlight the need for further validation across institutions and clinical workflows.

Related papers

• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services [49.70819009392778]
  Large Language Models (LLMs) and Multi-Agent Systems (MAS) offer opportunities to augment dispatchers.<n>This study aimed to develop and evaluate a taxonomy-grounded, multi-agent system for simulating realistic scenarios.
  arXiv  Detail & Related papers  (2025-10-24T08:01:21Z)
• Assessing Large Language Models for Structured Medical Order Extraction [0.6015898117103068]
  The MEDIQA-OE 2025 shared task focuses on extracting structured medical orders from extended conversational transcripts.<n>We present the Mason submission, which ranked 5th among 17 participating teams with 105 total submissions.<n>This few-shot configuration achieved an average F1 score of 37.76, with notable improvements in reason and provenance accuracy.
  arXiv  Detail & Related papers  (2025-10-12T06:56:10Z)
• MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs [5.40185721303932]
  This paper presents the methodology we applied in the MultiClinSUM shared task for summarising clinical case documents.<n>We used an Iterative Self-Prompting technique on large language models (LLMs) by asking LLMs to generate task-specific prompts.<n>We used lexical and embedding space metrics, ROUGE and BERT-score, to guide the model fine-tuning with epochs.
  arXiv  Detail & Related papers  (2025-09-09T11:52:16Z)
• Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM [6.26004554105527]
  We propose a novel approach to achieve the same results from unannotated full documents using general large language models (LLMs) with lower hardware and labor costs.<n>Our approach combines two major stages: named entity recognition (NER) and relation extraction (RE)<n>To enhance the effectiveness of prompt, we propose a five-part template structure and a scenario-based prompt design principles.
  arXiv  Detail & Related papers  (2025-05-02T07:33:20Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT [80.33783969507458]
  The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Recent studies have achieved promising results in automatic impression generation using large-scale medical text data. These models often require substantial amounts of medical text data and have poor generalization performance.
  arXiv  Detail & Related papers  (2023-04-17T17:13:42Z)
• Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse [53.797797404164946]
  The study highlights the difficulties faced in sharing tools and resources in this domain. We annotated a corpus of clinical documents according to 12 types of identifying entities. We build a hybrid system, merging the results of a deep learning model as well as manual rules.
  arXiv  Detail & Related papers  (2023-03-23T17:17:46Z)
• Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine [68.7814360102644]
  We propose the Re$3$Writer method with retrieval-augmented generation and knowledge-grounded reasoning. We demonstrate the effectiveness of our method in generating patient discharge instructions.
  arXiv  Detail & Related papers  (2022-10-23T16:34:39Z)
• Active learning for medical code assignment [55.99831806138029]
  We demonstrate the effectiveness of Active Learning (AL) in multi-label text classification in the clinical domain. We apply a set of well-known AL methods to help automatically assign ICD-9 codes on the MIMIC-III dataset. Our results show that the selection of informative instances provides satisfactory classification with a significantly reduced training set.
  arXiv  Detail & Related papers  (2021-04-12T18:11:17Z)
• A Systematic Review of Natural Language Processing Applied to Radiology Reports [3.600747505433814]
  This study systematically assesses recent literature in NLP applied to radiology reports. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.
  arXiv  Detail & Related papers  (2021-02-18T18:54:41Z)
• An Emergency Medical Services Clinical Audit System driven by Named Entity Recognition from Deep Learning [0.0]
  We present an automatic audit system based on both the structured and unstructured ambulance case records and clinical notes with a deep neural network-based named entities recognition model. Our approach yielded a named entity recognition model that could reliably identify clinical entities from unstructured paramedic free-text reports.
  arXiv  Detail & Related papers  (2020-07-07T16:32:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.