AI in healthcare: today’s research roundup — 24 October 2026

Several new studies examine how clinicians and patients are using large language models, and how reliable the output is. A spine-surgery study compared five LLMs' citation accuracy on clinician- versus patient-level questions: OpenEvidence, which indexes PubMed directly, scored perfectly, while Claude led the general-purpose models and Gemini had the highest fabrication rate — and all general-purpose models did worse when the question was framed as coming from a patient rather than a clinician. In a separate survey, 52 physicians ranked GPT-4 and Meta AI responses to real r/AskDocs questions against verified-physician answers and preferred the AI answers overall, even among physicians with more than 15 years of experience (mean rank 1.75 vs. 2.62). A third study had seven Duke surgical oncologists rate ChatGPT-4o's answers to 28 perioperative questions about hepatic, pancreatic, and colon cancer surgery: domain scores landed around 3.5-3.6 out of 5, but readability stayed at a college reading level (median grade ~14.6) and reviewers flagged a number of inaccuracies.

On the prognostic and diagnostic model side, an AI-powered multimodal model built from a multicenter, multinational head and neck cancer cohort is being used as a longitudinal surveillance tool for recurrence-free and overall survival after curative-intent surgery. A machine learning comparison for unresectable pancreatic cancer treated with chemoradiotherapy found XGBoost outperformed Cox, LASSO, random survival forest, and SVM models, reaching a C-index of 0.949. A 10-year, 45,000-discharge retrospective study from a Mexican tertiary hospital found XGBoost edged out random forest and logistic regression for predicting in-hospital death (AUC 0.916 vs. 0.898 and 0.904). A meta-analysis pooling nine studies on predicting pituitary adenoma consistency before surgery reinforced that MRI-based machine learning can estimate tumor firmness noninvasively, and a new external validation study tested an AI algorithm's reliability for detecting and segmenting brain metastases on MRI across multiple datasets.

Across both threads, today's articles point to the same theme as recent days: LLMs are increasingly competitive with, or even preferred over, human-written answers on narrow comprehension tasks, while traditional ML models continue to post strong discrimination on well-defined prognostic questions — with external validation and readability remaining the recurring caveats.

Sources

1. Large Language Model Hallucinations in Spine Surgery: A Comparative Analysis of Clinician vs Patient-Level Prompts
2. Assessment of Physician Preferences for Large Language Model-Generated Responses Across Geographic Regions and Clinical Experience Levels
3. Physician Perspectives on ChatGPT-4o as a Patient Resource for Abdominal Cancer Surgeries
4. Artificial intelligence-powered real-time multimodal model for predicting recurrence and survival in head and neck cancer
5. Application of machine learning for prognostic modeling in unresectable pancreatic cancer treated with chemoradiotherapy
6. Prediction of in-hospital death among patients admitted to a tertiary care hospital over the first 10 years
7. Machine learning-based models for preoperative prediction of pituitary adenoma consistency
8. Comprehensive performance testing and external validation of an AI algorithm to detect and segment brain metastases

Prepared with AI assistance from newly published research. See the sources for the original studies.