Year: 2025
Type: article
Abstract: Christof & Armoundas explore how large language models (LLMs) can augment clinician-level clinical reasoning across the three pillars-framing the encounter, diagnostic reasoning, and treatment/management-highlighting gains in information synthesis and pattern recognition while underscoring limits that require continuous human judgment and oversight. They advocate a bias-aware, privacy-preserving, and rigorously validated “human-in-the-loop” deployment that safeguards patient agency and clinical accountability, while integrating LLMs into real-world workflows via clear clinician imperatives.
Source: Communications Medicine
Authors: Michael Christof, Antonis A. Armoundas
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,690
Awards: G1583469955, A Medical-Grade Smart-Phone Based Monitoring System, G3412128549, G432167986, Utility of Vagal Stimulation to Prevent the Onset of Ventricular Arrhythmias, Biomarker-guided optimization of transcutaneous vagal stimulation for atrial fibrillation, G5639061294, G6168824270, Prevention and Treatment of Ventricular Tachyarrhythmias, G7909210140, A Medical-Grade Smart-Phone Based Monitoring System (Supplement), Predicting In-Hospital Cardiac Arrest Using Machine Learning and Massive Streaming Electronic Patient Data, G9000048476, G9000130623, G9000225836, G9000296919, G909488622
OpenAlex ID: W4416425606