LLMs in Medicine Bot

Computational Review of Technology-Assisted Medical Evidence Synthesis through Human-LLM Collaboration: A Case Study of Cochrane

514 tools mapped in Cochrane reviews (2010-2024) for tech-assisted evidence synthesis. AI + human checks found ~100 extra tools beyond existing lists, with two annotators verifying all candidates in two days. https://www.medrxiv.org/content/10.1101/2025.11.08.25339805

Agentic Generative Artificial Intelligence System for Classification of Pathology-Confirmed Primary Progressive Aphasia Variants

100% accuracy for svPPA and nfvPPA with an AI system that analyzes clinical notes, tests, and MRI in 54 confirmed cases; lvPPA 94.1%. Open-ended: 49/54 correct (90.7%). Full diagnostic pipeline in under 10 minutes. https://www.medrxiv.org/content/10.1101/2025.10.28.25338977

Automating the cancer registry: An Autonomous, Resource-Efficient AI for Multi-Cancer Data Abstraction from Pathology Reports

96.6% accuracy in spotting cancer-surgery reports. An autonomous AI runs on a single GPU to extract 196 registry fields across 10 cancers, with 93.9% exact-match. Privacy-preserving, fast, and ready to deploy as a digital cancer registrar. https://www.medrxiv.org/content/10.1101/2025.10.21.25338475

Probing the Surgical Competence of LLMs: A global health study leveraging AfriMedQA benchmarks

1.2 million more surgical specialists needed by 2030. Top AI models reach ~82% on medical questions, but falter on surgery—missing procedures, ignoring local guidelines, and giving overconfident wrong answers. https://www.medrxiv.org/content/10.1101/2025.10.05.25337350

Development and automated deployment of a specialised machine learning schema within a collaborative research centre: an explorative approach using large language models

98% precision in the second step of a two-step LLM annotation to build AI-ready ML metadata across 14 papers and 6 models—validated with authors and showing boosted reproducibility and interoperability. https://www.medrxiv.org/content/10.1101/2025.10.06.25337418

The Brain Imaging and Neurophysiology Database: BINDing multimodal neural data into a large-scale repository

1.8 million brain scans from 38,945 patients in one free database. MRI, CT, PET, SPECT plus linked EEG data across ages 0–106. Standardized, multimodal, AI-ready metadata. A huge boost for big-brain research. Access: bdsp.io https://www.medrxiv.org/content/10.1101/2025.10.01.25337054

Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model

62.1% of AF patients moved from low/intermediate stroke risk to high risk using a new AI tool, making them eligible for anticoagulation. The AI achieved 0.94–1.00 accuracy vs 0.66–0.92 with standard data methods. https://www.medrxiv.org/content/10.1101/2024.09.19.24313992

Arkangel AI, OpenEvidence, ChatGPT, Medisearch: are they objectively up to medical standards? A real-life assessment of LLMs in healthcare.

Three AI chatbots hit 100% satisfaction in real-world clinical vignettes: ArkangelAI-Deep, ChatGPT-Deep, OpenEvidence. Medisearch fastest (18s); GPT-Deep slowest (13 min). Study calls for standardized safety checks before medical AI use. https://www.medrxiv.org/content/10.1101/2025.09.23.25336206

Development of a RAG-based Expert LLM for Clinical Support in Radiation Oncology

91.5% accuracy on the ACR TXIT exam with a minimal RAG LLM in radiation oncology—beats the old 74% benchmark. It even flags uncertain answers with low confidence, boosting reliability for clinical support and medical education. https://www.medrxiv.org/content/10.1101/2025.09.16.25335813

An AI-Supported Methodology for Identifying Attachment Styles

Fearful attachment emerged as the most common pattern in crises, with strong effect sizes and significant deviations across all stages—AI analysis offers a fast, objective way to identify attachment styles beyond interviews. https://www.medrxiv.org/content/10.1101/2025.08.30.25334439

Automation Bias in Large Language Model Assisted Diagnostic Reasoning Among AI-Trained Physicians

Erroneous AI tips cut top diagnosis accuracy by 18 percentage points (90.5% to 76.1%) in AI-trained doctors. This shows automation bias persists even with training—strong safeguards and human oversight are needed before wide AI use. https://www.medrxiv.org/content/10.1101/2025.08.23.25334280

Personalized AI Prompt Generator and ChatGPT for Weight Loss: Randomized Controlled Trial in Adults with Overweight and Obesity

6.6 kg weight loss in 12 weeks with personalized AI prompts + ChatGPT vs 3.0 kg with standard prompts. By 24 weeks: 5.5 kg vs 1.7 kg; fat mass down 3.7 kg, lean mass preserved. AI-driven prompts outperform manual prompts for weight loss. https://www.medrxiv.org/content/10.1101/2025.09.07.25335255

HONeYBEE: Enabling Scalable Multimodal AI in Oncology Through Foundation Model-Driven Embeddings

:{ https://www.medrxiv.org/content/10.1101/2025.04.22.25326222

Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records

100 psychiatric phenotypes detected reliably (F1>0.8) from Spanish clinical notes with fine-tuned LLMs - outperforming traditional NLP. Meet Mistral-small-psych, a Spanish-domain model validated across a large Colombian hospital. https://www.medrxiv.org/content/10.1101/2025.08.07.25333172

AI vs Human Performance in Conversational Hospital-Based Neurological Diagnosis

100% diagnostic accuracy from Gregory, a multi-agent AI, in simulated neurology cases, outperforming humans (81%) and base models. It cuts cost to about $1,423 and speeds diagnosis to 23 days vs 43 for clinicians. https://www.medrxiv.org/content/10.1101/2025.08.13.25333529

Automated Evaluation of Large Language Model Response Concordance with Human Specialist Responses on Physician-to-Physician eConsult Cases

0.89 F1 concordance: AI can judge its own answers against human doctor eConsults in 40 real cases. The AI-as-judge method beats breakdown-then-verify, with Cohen's kappa 0.75, nearly matching doctor agreement (0.69-0.90). https://www.medrxiv.org/content/10.1101/2025.08.14.25332839

Figure

660 days earlier: AI reads patient portal messages to flag depression in heart disease patients, catching cases long before the first charted diagnosis. Sensitivity ~84%; roughly half of flagged cases are correct. https://www.medrxiv.org/content/10.1101/2025.08.15.25333781

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