Comparison of single-shot neuraxial morphine and erector spinae plane block on quality of recovery after major open gastrointestinal surgeries: A prospective, randomized trial.

Background: It is crucial to assess a patient's quality of recovery after major surgery. This study aims to compare the effect of neuraxial morphine and bilateral erector spinae plane block on quality of recovery in the first 48 postoperative hours in patients undergoing open upper abdominal surgeries.

Methods: This prospective, triple-arm, randomized study was performed to compare the effect of neuraxial morphine (intrathecal morphine, thoracic epidural) and erector spinae plane block on postoperative recovery.

Prompt Engineering and Follow-Up Questioning Improves the Readability of Spine Surgery Questions in Large Language Models.

Introduction: The field of spine surgery is complex, and patient education material within the field are often written at a reading level that exceeds the recommended standard. Large language models (LLMs), such as ChatGPT, have shown potential for generating educational content, but require further investigation regarding readability and adaptability in responding to follow-up questions. The purpose of this study is to evaluate which model and prompting strategies improve the readability the most, especially in newer models.

Beyond Post hoc Explanations: A Comprehensive Framework for Accountable AI in Medical Imaging Through Transparency, Interpretability, and Explainability.

The integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic capabilities, yet the black-box nature of deep learning models poses significant challenges for clinical adoption. Current explainable AI (XAI) approaches, including SHAP, LIME, and Grad-CAM, predominantly focus on post hoc explanations that may inadvertently undermine clinical decision-making by providing misleading confidence in AI outputs. This paper presents a systematic review and meta-analysis of 67 studies (covering 23 radiology, 19 pathology, and 25 ophthalmology applications) evaluating XAI fidelity, stability, and performance trade-offs across medical imaging modalities.