ReviewNPJ digital medicine2022
A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From admission to discharge: a systematic review of clinical natural language processing along the patient journey.BMC medical informatics and decision making · 2024Pooled it
- The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.Age and ageing · 2024Pooled it
- Assessing pediatric clinician adherence to the guidelines for prevention of peanut allergy: a natural language processing study.BMC medical informatics and decision making · 2025Trial
- Hierarchical multi-label structuring of Japanese SOAP clinical notes with large language models.Scientific reports · 2026Article
- A practical guide to the implementation of AI in orthopaedic research-Part 4: Prerequisites for a successful orthopedics AI-driven project in terms of interdisciplinary collaboration, data management, ethical approval and technology.Journal of experimental orthopaedics · 2026Review
- Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study.JMIR medical informatics · 2026Article
- MTB-ImmunogenKG: An LLM-assisted knowledge graph for antigen selection in tuberculosis vaccine research.Biosafety and health · 2026Article
- WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis.NPJ digital medicine · 2026Article
- Clinical evaluation of MiADE: a natural language processing system for assisting structured diagnosis recording at the point of care.BMJ health & care informatics · 2026Article
- Comparison of local large language models for extraction of signs and symptoms data from electronic health records.PloS one · 2026Article
- Topic Modeling of Nursing Documentation in Hemodialysis Units: A Mixed-Methods Study of Nursing Surveillance Activities.Journal of nursing management · 2026Observational
- Clinical temporal relation extraction with long-context transformers: a robustness study on MIMIC-III and MIMIC-IV.Frontiers in digital health · 2026Article
- Artificial intelligence methods to detect heart failure with preserved ejection fraction within electronic health records: an equitable disease detection model.European heart journal. Digital health · 2026Article
- Key aspects of fine-tuning and applying LLM-as-a-judge for clinical data summaries in the radiological workflow.Frontiers in artificial intelligence · 2026Article
- Systematic literature review and narrative synthesis of the use of natural language processing to triage outpatient referrals.Frontiers in health services · 2026Review
- Large language models accurately identify immunosuppression in intensive care unit patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
- Design and implementation of a natural language processing system at the point of care: MiADE (medical information AI data extractor).BMC medical informatics and decision making · 2025Article
- Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data.Stroke · 2025Article
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.
Identifiers
What Socratic holds
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 Socratic graph.