Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study.

Background: An electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments).

Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?

Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders.

Streams, rivers and data lakes: an introduction to understanding modern electronic healthcare records.

As foundation doctors, we have often found ourselves informing patients that a certain aspect of their medical information cannot be immediately found, either because it is on an electronic system we cannot access, or it is in a hospital that is unlinked to our own. Unsurprisingly, this frequently leaves patients flabbergasted and confused. We started to wonder: if patients' data are entered onto an electronic system: where do those data go? If medical data are searched for, where do those data come from? Why are there so many hidden sources of information that clinicians cannot access? In an ever-increasing digital sphere, electronic data will be the future of holistic health and social care planning, impacting every clinician's day-to-day role.

AI chatbots not yet ready for clinical use.

As large language models (LLMs) expand and become more advanced, so do the natural language processing capabilities of conversational AI, or "chatbots". OpenAI's recent release, ChatGPT, uses a transformer-based model to enable human-like text generation and question-answering on general domain knowledge, while a healthcare-specific Large Language Model (LLM) such as GatorTron has focused on the real-world healthcare domain knowledge. As LLMs advance to achieve near human-level performances on medical question and answering benchmarks, it is probable that Conversational AI will soon be developed for use in healthcare.

Curing somatisation-induced paraplegia with experimental dice-based affective modulation.

Following a minor meniscal injury to his right knee, a previously fit and well 58-year-old man developed profound somatisation leading to paraplegia. The patient developed a deep-seated belief that any exercise or walking would cause irreparable damage to his knee. Over the course of 2 years his, mobility reduced from active mountaineering to walking a short distance, and finally to paraplegia.

Machine learning-enabled multitrust audit of stroke comorbidities using natural language processing.

Background And Purpose: With the increasing adoption of electronic records in the health system, machine learning-enabled techniques offer the opportunity for greater computer-assisted curation of these data for audit and research purposes. In this project, we evaluate the consistency of traditional curation methods used in routine clinical practice against a new machine learning-enabled tool, MedCAT, for the extraction of the stroke comorbidities recorded within the UK's Sentinel Stroke National Audit Programme (SSNAP) initiative.

Methods: A total of 2327 stroke admission episodes from three different National Health Service (NHS) hospitals, between January 2019 and April 2020, were included in this evaluation.

Dietary nitrate prevents progression of carotid subclinical atherosclerosis through blood pressure-independent mechanisms in patients with or at risk of type 2 diabetes mellitus.

Aims: To test if 6 months' intervention with dietary nitrate and spironolactone could affect carotid subclinical atherosclerosis and stiffness, respectively, vs. placebo/doxazosin, to control for blood pressure (BP).

Methods: A subgroup of participants in our double-blind, randomized-controlled, factorial VaSera trial had carotid imaging.

Effect of dapagliflozin in patients with heart failure.

McMurray JJV, Solomon SD, Inzucchi SE, Dapagliflozin in patients with heart failure and reduced ejection fraction. 2019;381:1995-2008. Dr Joshua Au Yeunga, Clinical Pharmacology, St Thomas' Hospital, London, UK and Dr Teck Khong, Clinical Pharmacology, St George's, University of London, UK.

Unusual case of anxiety: trichloroethylene neurotoxicity.

I present an uncommon case of recurrent, intractable anxiety that was presented acutely and slowly evolved into a chronic debilitating condition. A young previously fit and healthy 24-year-old patient presents with somewhat atypical symptoms of anxiety. Full medical work-up including examination, blood, ECG electrocardiogram, electroencephalogram and CT of the head was unremarkable.