Myocardial Infarction (MI) remains one of the leading causes of death worldwide. Electrocardiogram (ECG) is a reliable diagnostic tool for MI and its interpretation requires cardiology expertise. Advancement of multimodal large language models (MLLMs) attracted attempts to interpret ECG images, but prior studies showed low accuracies. To address the issue, we investigated a few strategies for pretrained MLLMs: (1) inclusion of domain diagnostic prompt instruction, (2) few-shot technique by providing annotated examples in the prompt, and (3) adjustment of model hyperparameters. On a corpus of 928 annotated 12-lead ECG images, our results utilizing Google Gemini 2.5 Pro and OpenAI GPT-4o for MI versus non-MI classification showed noticeable performance improvements. With 30-shot prompting, Gemini achieved 76.08 ∼ 30
Accurate localization of the SubThalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery is critical for therapeutic efficacy and is commonly supported by intraoperative MicroElectrode Recordings (MERs). While deep learning approaches have shown promising performance in automatic STN identification, their limited transparency hinders their clinical adoption. In this work, we present an interpretable deep learning pipeline for MERs classification coupled with a structured validation framework aimed at assessing the clinical relevance of the model reasoning. To this aim, the classification output from a patch-based convolutional neural network with self-attention is combined with Grad-CAM relevance maps. Alignment between relevance maps and manual annotations from 3 expert neurologists with 27, 20, and 11 years of experience was quantified using overlap-based metrics, while perceived transparency, usefulness, and trustworthiness were assessed through Likert-scale questionnaires. Results show competitive classification performance (0.92 ± 0.07 AUC) and consistent agreement between automatic explanations and expert reasoning, with a maximum Dice Score of 0.77 ± 0.11, alongside high clinician acceptance and perceived interpretability. These findings suggest that structured, expert-centred validation of XAI can provide a meaningful contribution toward trustworthy AI-assisted decision support systems in intraoperative neurosurgery.
This work proposes a voice-based clinical decision support tool that detects a generalized behavioral health condition, defined by Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD), from spontaneous speech. We compare Random Forest, XGBoost, and fully connected deep neural network classifiers trained on high-dimensional voice representations, including X-vector, Wav2Vec2, TRILLsson, and HuBERT embeddings. Models are trained using validated symptom instruments, including the PHQ-8 and GAD-7. To enhance clinical safety, we incorporate an ‘uncertain’ classification category for low-confidence samples. Our model achieves a sensitivity of 0.68 and specificity of 0.80, demonstrating that a consolidated vocal biomarker metric provides a scalable, non-invasive mechanism to identify health conditions in both in-clinic and remote settings.
This systematic review evaluates the effectiveness of Artificial Intelligence (AI) in preventing cardiovascular disease (CVD). We conducted a systematic review using Joanna Briggs Institute (JBI) methodology, with the protocol registered under CRD42022369548 on PROSPERO. We included studies on all populations who receive or provide care and used AI for risk prediction and prevention of CVD. We searched seven databases from 1999 till October 2022. Two reviewers independently screened the identified records and extracted data from the included studies. We conducted a subgroup analysis on studies that compare AI to standard care. We assessed the risk of bias using a Modified IJMEDI and PROBAST tool, and data are synthesized in narrative form, considering various study aspects. This review adheres to PRISMA guidelines for systematic reviews and meta-analyses. After screening 7,505 identified records, 266 articles were included in the review. Risk prediction and early disease diagnosis were the most common prevention modalities. The subgroup analysis of 16 studies that compared these interventions with standard care included 51,687,627 participants. The Framingham Risk Score was the most used comparator, reported in 8 of the 16 studies (50
The design and evaluation of Retrieval-Augmented Generation (RAG) pipelines remain fragmented, with system components often tailored to specific datasets and tasks. In this paper, we propose STRAGMED, a simple yet effective RAG pipeline to provide a standardized baseline for system engineering and retrieval evaluation. Our experiments show that a hybrid pipeline combining a retriever, reranking at each step, and Reciprocal Rank Fusion (RRF), yields consistent improvements across medical datasets, outperforming retrieval-only and individually reranked outputs. These results highlight a robust and generalizable baseline configuration for medical RAG systems, enabling researchers to focus on task-specific optimizations such as query augmentation, model selection, and retrieval refinement.
