Automated Mass Extraction of Over 680,000 PICOs from Clinical Study Abstracts Using Generative AI: A Proof-of-Concept Study.

Background: Generative artificial intelligence (GenAI) shows promise in automating key tasks involved in conducting systematic literature reviews (SLRs), including screening, bias assessment and data extraction. This potential automation is increasingly relevant as pharmaceutical developers face challenging requirements for timely and precise SLRs using the population, intervention, comparator and outcome (PICO) framework, such as those under the impending European Union (EU) Health Technology Assessment Regulation 2021/2282 (HTAR). This proof-of-concept study aimed to evaluate the feasibility, accuracy and efficiency of using GenAI for mass extraction of PICOs from PubMed abstracts.

An exploration of service use pattern changes and cost analysis following implementation of community perinatal mental health teams in pregnant women with a history of specialist mental healthcare in England: a national population-based cohort study.

Background: The National Health Service in England pledged >£365 million to improve access to mental healthcare services via Community Perinatal Mental Health Teams (CPMHTs) and reduce the rate of perinatal relapse in women with severe mental illness. This study aimed to explore changes in service use patterns following the implementation of CPMHTs in pregnant women with a history of specialist mental healthcare in England, and conduct a cost-analysis on these changes.

Methods: This study used a longitudinal cohort design based on existing routine administrative data.

Artificial Intelligence to Automate Network Meta-Analyses: Four Case Studies to Evaluate the Potential Application of Large Language Models.

Background: The emergence of artificial intelligence, capable of human-level performance on some tasks, presents an opportunity to revolutionise development of systematic reviews and network meta-analyses (NMAs). In this pilot study, we aim to assess use of a large-language model (LLM, Generative Pre-trained Transformer 4 [GPT-4]) to automatically extract data from publications, write an R script to conduct an NMA and interpret the results.

Methods: We considered four case studies involving binary and time-to-event outcomes in two disease areas, for which an NMA had previously been conducted manually.

Artificial Intelligence to Automate Health Economic Modelling: A Case Study to Evaluate the Potential Application of Large Language Models.

Background: Current generation large language models (LLMs) such as Generative Pre-Trained Transformer 4 (GPT-4) have achieved human-level performance on many tasks including the generation of computer code based on textual input. This study aimed to assess whether GPT-4 could be used to automatically programme two published health economic analyses.

Methods: The two analyses were partitioned survival models evaluating interventions in non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC).

Community perinatal mental health teams and associations with perinatal mental health and obstetric and neonatal outcomes in pregnant women with a history of secondary mental health care in England: a national population-based cohort study.

Background: Women with a pre-existing severe mental disorder have an increased risk of relapse after giving birth. We aimed to evaluate associations of the gradual regional implementation of community perinatal mental health teams in England from April, 2016, with access to mental health care and with mental health, obstetric, and neonatal outcomes.

Methods: For this cohort study, we used the national dataset of secondary mental health care provided by National Health Service England, including mental health-care episodes from April 1, 2006, to March 31, 2019, linked at patient level to the Hospital Episode Statistics, and birth notifications from the Personal Demographic Service.