NHS England's £10bn AI rollout promises transformation—but The BMJ warns the evidence framework is dangerously weak. While medicines need rigorous tria… #Artificialintelligence #Clinicalevidence #DigitalHealth #Healthtechnologyassessment #NHS

Background: Systematic literature reviews (SLRs) are essential for evidence synthesis in health research but remain labor-intensive, especially at the screening stage. Manual review of titles and abstracts requires substantial human effort, while existing automation tools still have limited adoption in health technology assessment. The EQ-5D questionnaire, a widely used patient-reported outcome measure for health-related quality of life, provides data that frequently underpin reimbursement and policy decisions. Objective: This pilot study evaluated whether recent large language models (LLMs) can support the identification of publications reporting EQ-5D data in PubMed records, using only publicly available metadata (title, abstract, and keywords). Methods: A total of 200 publications retrieved through the EuroQol PubMed filter were manually labeled by experts as reporting or not reporting EQ-5D data. The dataset was split into stratified training, validation, and test subsets. Several machine learning approaches were compared, including a Naïve Bayes baseline using bag-of-words features, a decision-tree model based on full-text keyword occurrence, and transformer-based LLMs (Bidirectional Encoder Representations from Transformers [BERT], Biomedical BERT [BioBERT], Scientific BERT [SciBERT], and Biomedical Language Understanding Evaluation BERT [BlueBERT]). Both classifier-only and fine-tuned configurations were tested across multiple learning rates. Model performance was assessed using accuracy, precision, recall, and -score. Results: Baseline approaches achieved near-random test performance (accuracy around 0.53). Classifier-only LLMs modestly improved results (accuracy up to 0.64 with SciBERT). Fine-tuned models substantially outperformed these baselines, with BERT and BioBERT achieving the best performance (accuracy=0.70; -score=0.68). In screening-oriented evaluation, this configuration achieved 90.0% sensitivity, 40.0% specificity, and 6 false negatives on the held-out test set. The models reproduced human screening tendencies despite the small dataset size, demonstrating the technical #feasibility of LLM-assisted article selection. Conclusions: This study provides the first demonstration of LLM-assisted identification of EQ-5D data in biomedical literature. The findings support technical #feasibility but do not establish a reliable stand-alone automated screening tool. Although limited by dataset size, the proposed workflow is reproducible and adaptable to other patient-reported outcome measures. Because validation was based on a single small train-validation-test split, the results should be interpreted as preliminary; future work will scale data collection, include statistical testing, and explore semisupervised learning to further reduce manual screening workload.

◤ Breaking News ◤ HTAi Holiday Hours As the end of 2024 approaches, HTAi wishes everyone a joyful and restful time with family and friends.Please note that our offices will be closed from December

HTAi members are the vanguard of Global Health Technology Assessment.

Learn how new frameworks for HTAs can better capture the full value of medical innovations, including broader social impacts.

Learn how to streamline health economic models for multicountry adaptations, ensuring efficiency, compliance, and cost-effectiveness in HTA submissions.

Amgen's Lumykras will no longer be funded by the Cancer Drugs Fund for KRAS-mutated lung cancer, says NHS reimbursement authority NICE.