Recent advancements in large language models (LLMs) have led to multimodal LLMs (MLLMs), which integrate multiple data modalities beyond text. Although MLLMs show promise, there is a gap in the literature that empirically demonstrates their impact in healthcare. This paper summarizes the applications of MLLMs in healthcare, highlighting their potential to transform health practices. A rapid literature review was conducted in August 2024 using World Health Organization (WHO) rapid-review methodology and PRISMA standards, with searches across four databases (Scopus, Medline, PubMed and ACM Digital Library) and top-tier conferences-including NeurIPS, ICML, AAAI, MICCAI, CVPR, ACL and EMNLP. Articles on MLLMs healthcare applications were included for analysis based on inclusion and exclusion criteria. The search yielded 115 articles, 39 included in the final analysis. Of these, 77% appeared online (preprints and published) in 2024, reflecting the emergence of MLLMs. 80% of studies were from Asia and North America (mainly China and US), with Europe lagging. Studies split evenly between pre-built MLLMs evaluations (60% focused on GPT versions) and custom MLLMs/frameworks development with task-specific customizations. About 81% of studies examined MLLMs for diagnosis and reporting in radiology, pathology, and ophthalmology, with additional applications in education, surgery, and mental health. Prompting strategies, used in 80% of studies, improved performance in nearly half. However, evaluation practices were inconsistent with 67% reported accuracy. Error analysis was mostly anecdotal, with only 18% categorized failure types. Only 13% validated explainability through clinician feedback. Clinical deployment was demonstrated in just 3% of studies, and workflow integration, governance, and safety were rarely addressed. MLLMs offer substantial potential for healthcare transformation through multimodal data integration. Yet, methodological inconsistencies, limited validation, and underdeveloped deployment strategies highlight the need for standardized evaluation metrics, structured error analysis, and human-centered design to support safe, scalable, and trustworthy clinical adoption.

Classification models that provide human-interpretable explanations enhance clinicians' trust and usability in medical image diagnosis. One research focus is the integration and prediction of pathology-related visual attributes used by radiologists alongside the diagnosis, aligning AI decision-making with clinical reasoning. Radiologists use attributes like shape and texture as established diagnostic criteria and mirroring these in AI decision-making both enhances transparency and enables explicit validation of model outputs. However, the adoption of such models is limited by the scarcity of large-scale medical image datasets annotated with these attributes. To address this challenge, we propose synthesizing attribute-annotated data using a generative model. We enhance the Diffusion Model with attribute conditioning and train it using only 20 attribute-labeled lung nodule samples from the LIDC-IDRI dataset. Incorporating its generated images into the training of an explainable model boosts performance, increasing attribute prediction accuracy by 13.4% and target prediction accuracy by 1.8% compared to training with only the small real attribute-annotated dataset. This work highlights the potential of synthetic data to overcome dataset limitations, enhancing the applicability of explainable models in medical image analysis.

Liver diseases pose a significant global health challenge due to their silent progression and high mortality. Proper interpretation of radiology reports is essential for the evaluation and management of these conditions but is limited by variability in reporting styles and the complexity of unstructured medical language. In this context, Natural Language Processing (NLP) techniques and Large Language Models (LLMs) have emerged as promising tools to extract relevant clinical information from unstructured liver radiology reports. This work reviews, from a practical point of view, the current state of NLP and LLM applications for liver disease classification, clinical feature extraction, diagnostic support, and staging from reports. It also discusses existing limitations, such as the need for high-quality annotated data, lack of explainability, and challenges in clinical integration. With responsible and validated implementation, these technologies have the potential to transform liver clinical management by enabling faster and more accurate diagnoses and optimizing radiology workflows, ultimately improving patient care in liver diseases.

The rapid evolution of artificial intelligence, especially in large language models (LLMs), has significantly impacted various domains, including healthcare. In chest X-ray (CXR) analysis, previous studies have employed LLMs, but with limitations: either underutilizing the LLMs' capability for multitask learning or lacking clinical accuracy. This article presents M4CXR, a multimodal LLM designed to enhance CXR interpretation. The model is trained on a visual instruction-following dataset that integrates various task-specific datasets in a conversational format. As a result, the model supports multiple tasks such as medical report generation (MRG), visual grounding, and visual question answering (VQA). M4CXR achieves state-of-the-art clinical accuracy in MRG by employing a chain-of-thought (CoT) prompting strategy, in which it identifies findings in CXR images and subsequently generates corresponding reports. The model is adaptable to various MRG scenarios depending on the available inputs, such as single-image, multiimage, and multistudy contexts. In addition to MRG, M4CXR performs visual grounding at a level comparable to specialized models and demonstrates outstanding performance in VQA. Both quantitative and qualitative assessments reveal M4CXR's versatility in MRG, visual grounding, and VQA, while consistently maintaining clinical accuracy.

Substance use disorders (SUD) are chronic, relapsing conditions marked by high variability in treatment response and frequent relapse. This variability arises from complex interactions among behavioral, environmental, and biological factors unique to each individual. Precision medicine, which tailors treatment to patient-specific characteristics, offers a promising avenue to address these challenges. This review explores key factors influencing SUD, including severity, comorbidities, drug use motives, polysubstance use, cognitive impairments, and biological and environmental influences. Advanced neuroimaging, such as MRI and PET, enables patient subtyping by identifying altered brain mechanisms, including reward, relief, and cognitive pathways, and striatal dopamine D_2/3 receptor binding. Pharmacogenetic and epigenetic studies uncover how variations in dopaminergic, serotoninergic, and opioidergic systems shape treatment outcomes. Emerging biomarkers, such as neurofilament light chain, offer non-invasive relapse monitoring. Multifactorial models integrating behavioral and neural markers outperform single-factor approaches in predicting treatment success. Machine learning refines these models, while longitudinal and preclinical studies support individualized care. Despite translational hurdles, precision medicine offers transformative potential for improving SUD treatment outcomes.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder and the early diagnosis is crucial for managing symptoms and slowing disease progression. This paper proposes a framework named Federated Learning Enabled Waterwheel Shuffled Shepherd Optimization-based Efficient-Fuzzy Deep Maxout Network (FedL_WSSO based Eff-FDMNet) for PD detection and classification. In local training model, the input image from the database "Image and Data Archive (IDA)" is given for preprocessing that is performed using Gaussian filter. Consequently, image augmentation takes place and feature extraction is conducted. These processes are executed for every input image. Therefore, the collected outputs of images are used for PD detection using Shepard Convolutional Neural Network Fuzzy Zeiler and Fergus Net (ShCNN-Fuzzy-ZFNet). Then, PD classification is accomplished using Eff-FDMNet, which is trained using WSSO. At last, based on CAViaR, local updation and aggregation are changed in server. The developed method obtained highest accuracy as 0.927, mean average precision as 0.905, lowest false positive rate (FPR) as 0.082, loss as 0.073, Mean Squared Error (MSE) as 0.213, and Root Mean Squared Error (RMSE) as 0.461. The high accuracy and low error rates indicate that the potent framework can enhance patient outcomes by enabling more reliable and personalized diagnosis.

To explore the potential role of generative artificial intelligence (GenAI) in enhancing patient counseling following prenatal ultrasound diagnosis of fetal malformations, with an emphasis on clinical utility, patient comprehension, and ethical implementation. The detection of fetal anomalies during the mid-trimester ultrasound is emotionally distressing for patients and presents significant challenges in communication and decision-making. Generative AI tools, such as GPT-4 and similar models, offer novel opportunities to support clinicians in delivering accurate, empathetic, and accessible counseling while preserving the physician's central role. We present a narrative review and applied framework illustrating how GenAI can assist obstetricians before, during, and after the fetal anomaly scan. Use cases include lay summaries, visual aids, anticipatory guidance, multilingual translation, and emotional support. Tables and sample prompts demonstrate practical applications across a range of anomalies.

Early and accurate detection of the bone fracture is paramount to initiating treatment as early as possible and avoiding any delay in patient treatment and outcomes. Interpretation of X-ray image is a time consuming and error prone task, especially when resources for such interpretation are limited by lack of radiology expertise. Additionally, deep learning approaches used currently, typically suffer from misclassifications and lack interpretable explanations to clinical use. In order to overcome these challenges, we propose an automated framework of bone fracture detection using a VGG-19 model modified to our needs. It incorporates sophisticated preprocessing techniques that include Contrast Limited Adaptive Histogram Equalization (CLAHE), Otsu's thresholding, and Canny edge detection, among others, to enhance image clarity as well as to facilitate the feature extraction. Therefore, we use Grad-CAM, an Explainable AI method that can generate visual heatmaps of the model's decision making process, as a type of model interpretability, for clinicians to understand the model's decision making process. It encourages trust and helps in further clinical validation. It is deployed in a real time web application, where healthcare professionals can upload X-ray images and get the diagnostic feedback within 0.5 seconds. The performance of our modified VGG-19 model attains 99.78\% classification accuracy and AUC score of 1.00, making it exceptionally good. The framework provides a reliable, fast, and interpretable solution for bone fracture detection that reasons more efficiently for diagnoses and better patient care.

Breast MRI provides high-resolution volumetric imaging critical for tumor assessment and treatment planning, yet manual interpretation of 3D scans remains labor-intensive and subjective. While AI-powered tools hold promise for accelerating medical image analysis, adoption of commercial medical AI products remains limited in low- and middle-income countries due to high license costs, proprietary software, and infrastructure demands. In this work, we investigate whether the Segment Anything Model 2 (SAM2) can be adapted for low-cost, minimal-input 3D tumor segmentation in breast MRI. Using a single bounding box annotation on one slice, we propagate segmentation predictions across the 3D volume using three different slice-wise tracking strategies: top-to-bottom, bottom-to-top, and center-outward. We evaluate these strategies across a large cohort of patients and find that center-outward propagation yields the most consistent and accurate segmentations. Despite being a zero-shot model not trained for volumetric medical data, SAM2 achieves strong segmentation performance under minimal supervision. We further analyze how segmentation performance relates to tumor size, location, and shape, identifying key failure modes. Our results suggest that general-purpose foundation models such as SAM2 can support 3D medical image analysis with minimal supervision, offering an accessible and affordable alternative for resource-constrained settings.

Sports injuries are a significant concern for athletes at all levels of competition, ranging from acute traumas to chronic conditions. Prompt diagnosis and effective treatment are crucial for an athlete's recovery and quality of life. Traditionally, sports injury diagnosis has relied on clinical assessments, patient history, and basic imaging techniques such as X-rays, ultrasound, and magnetic resonance imaging (MRI). However, recent technological advancements have revolutionized the field of sports medicine, offering more accurate diagnoses and targeted treatment strategies. High-resolution MRI and CT scans provide detailed images of deep tissue injuries, while advanced ultrasound technology enables on-field diagnostics. Wearable sensor devices and machine learning algorithms allow real-time monitoring of an athlete's movements and physical loads, facilitating early intervention and injury risk prediction. Regenerative medicine, including stem cell therapy and tissue engineering, has emerged as a transformative approach to healing damaged tissues and reducing treatment time. Despite the challenges of high costs, lack of skilled personnel, and ethical considerations, the integration of artificial intelligence and machine learning into sports medicine holds immense potential for revolutionizing injury prevention and management. As these advancements continue to evolve, they are expected to extend athletes' careers and enhance their overall quality of life. This review summarizes conventional methods to diagnose and manage injuries and provides insights into the recent advancements in the field of sports science and medicine. It also states future outlook on the diagnosis and treatment of sports injuries.