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. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation.

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. In this article we discuss the potential and compare the performance of two different approaches to generative pretrained transformers-ChatGPT, the most widely used general conversational LLM, and Foresight, a GPT (generative pretrained transformer) based model focused on modelling patients and disorders. The comparison is conducted on the task of forecasting relevant diagnoses based on clinical vignettes. We also discuss important considerations and limitations of transformer-based chatbots for clinical use.

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. Medical investigations were normal and organic causes were ruled out. Conventional therapy was exhausted, a number of medications were trialled over 5 years, including selective serotonin reuptake inhibitors (SSRIs) and antipsychotics without success. Eventually, with a combination of cognitive behavioural therapy, physiotherapy and a novel experimental therapy where the patient rolled dice and acted according to the roll results, the patient was able to literally and metaphorically get back on his feet.

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. In addition, current clinical curation methods (SSNAP) and the machine learning-enabled method (MedCAT) were compared against a subsample of 200 admission episodes manually reviewed by our study team. Performance metrics of sensitivity, specificity, precision, negative predictive value, and F1 scores are reported. RESULTS: The reporting of stroke comorbidities with current clinical curation methods is good for atrial fibrillation, hypertension, and diabetes mellitus, but poor for congestive cardiac failure. The machine learning-enabled method, MedCAT, achieved better performances across all four assessed comorbidities compared with current clinical methods, predominantly driven by higher sensitivity and F1 scores. CONCLUSIONS: We have shown machine learning-enabled data collection can support existing clinical and service initiatives, with the potential to improve the quality and speed of data extraction from existing clinical repositories. The scalability and flexibility of these new machine-learning tools, therefore, present an opportunity to revolutionize audit and research methods.

Effect of dapagliflozin in patients with heart failure.

Commentary on: McMurray JJV, Solomon SD, Inzucchi SE, et al Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med 2019;381:1995-2008. Commentary by: Dr Joshua Au Yeunga, Clinical Pharmacology, St Thomas' Hospital, London, UK and Dr Teck Khong, Clinical Pharmacology, St George's, University of London, UK. Series Editor: Dr Teck Khong, DTB Associate Editor, 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. When the history was explored in detail, it was revealed he worked in the navy and was exposed to a neurotoxic solvent, trichloroethylene. This case highlights the importance of eliciting a detailed occupational history, particularly paying attention to patient demographics such as occupation and presenting symptoms that do not readily fit into diagnostic criteria.