Neuroimaging-Based Responses to Blood Pressure Augmentation in Acute Ischaemic Stroke: A Systematic Review.

Introduction: In acute ischaemic stroke, the key treatment to reduce infarct growth is reperfusion, achieved through thrombolysis, endovascular thrombectomy, or endogenous reperfusion. Prior to definitive reperfusion therapy, blood pressure augmentation may enhance cerebral perfusion and reduce interim infarct growth. This study aimed to summarise the existing evidence from randomised controlled trials on the use of imaging for patient selection and the assessment of blood pressure augmentation in acute ischaemic stroke.

Computer-Assisted Protocol-Adherent Blood Lipid Evaluation in Vascular Outpatients (CAPABLE-Vascular).

The lack of availability of test results in vascular surgery outpatient clinics impedes the medical management of vascular risk factors, such as dyslipidaemia and diabetes mellitus. This study sought to evaluate the feasibility of using computer-assisted processes to promote the ordering of routine investigations to promote this management. After consultation with specialist clinicians, clinician-programmers developed a rule-based system to facilitate the ordering of lipid studies and HbA1c prior to vascular clinic appointments.

Epilepsy surgery candidate identification with artificial intelligence: An implementation study.

Background: To (a) evaluate the effect of a machine learning algorithm in the identification of patients suitable for epilepsy surgery evaluation, and (b) examine the performance of a large language model (LLM) in the collation of key pieces of information pertaining to epilepsy surgery evaluation referral.

Methods: Artificial intelligence analyses were performed for all patients seen in the epilepsy or first seizure clinic at a tertiary hospital over a 12-month period. This study design was intended to emulate a case review that could subsequently be conducted periodically (e.

Better off alone? Artificial intelligence can demonstrate superior performance without clinician input.

Recent studies challenge the assumption that human-artificial intelligence (AI) collaboration is universally optimal, highlighting tasks where AI alone outperforms combined efforts. This viewpoint discusses the reasons behind these findings, explores influences on synergy and emphasises the importance of identifying when clinicians add net benefit to AI performance. Maximising patient outcomes may require accepting AI autonomy in certain scenarios within healthcare practice.

Machine-learning versus traditional approaches to predict all-cause mortality for acute coronary syndrome: a systematic review and meta-analysis.

Background: Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the GRACE and TIMI risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis.