AI in Breast Cancer Imaging: An Update and Future Trends

Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

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An overview of methods and techniques in multimodal data fusion with application to healthcare

International Journal of Data Science and Analytics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

Language: Английский

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Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(2), P. 572 - 572

Published: Jan. 17, 2025

Background: Several artificial intelligence systems based on large language models (LLMs) have been commercially developed, with recent interest in integrating them for clinical questions. Recent versions now include image analysis capacity, but their performance gastroenterology remains untested. This study assesses ChatGPT-4's interpreting images. Methods: A total of 740 images from five procedures-capsule endoscopy (CE), device-assisted enteroscopy (DAE), endoscopic ultrasound (EUS), digital single-operator cholangioscopy (DSOC), and high-resolution anoscopy (HRA)-were included analyzed by ChatGPT-4 using a predefined prompt each. predictions were compared to gold standard diagnoses. Statistical analyses accuracy, sensitivity, specificity, positive predictive value (PPV), negative (NPV), area under the curve (AUC). Results: For CE, demonstrated accuracies ranging 50.0% 90.0%, AUCs 0.50-0.90. DAE, model an accuracy 67.0% (AUC 0.670). EUS, system showed 0.488 0.550 differentiation between pancreatic cystic solid lesions, respectively. The LLM differentiated benign malignant biliary strictures AUC 0.550. HRA, overall 47.5% 67.5%. Conclusions: suboptimal diagnostic interpretation across several techniques, highlighting need continuous improvement before adoption.

Language: Английский

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A review of medical text analysis: Theory and practice

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103024 - 103024

Published: Feb. 1, 2025

Language: Английский

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From screens to scenes: A survey of embodied AI in healthcare

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103033 - 103033

Published: Feb. 1, 2025

Language: Английский

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Reproduction of Original Glioblastoma and Brain Metastasis Research Findings Using Synthetic Data

World Neurosurgery, Journal Year: 2025, Volume and Issue: 196, P. 123808 - 123808

Published: March 13, 2025

Language: Английский

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Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine

FEBS Open Bio, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Medical digital twins (MDTs) are virtual representations of patients that simulate the biological, physiological, and clinical processes individuals to enable personalized medicine. With increasing complexity omics data, particularly multiomics, there is a growing need for advanced computational frameworks interpret these data effectively. Foundation models (FMs), large‐scale machine learning pretrained on diverse types, have recently emerged as powerful tools improving interpretability decision‐making in precision This review discusses integration FMs into MDT systems, their role enhancing multiomics data. We examine current challenges, recent advancements, future opportunities leveraging analysis MDTs, with focus application

Language: Английский

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Evaluating AI-generated patient education materials for spinal surgeries: Comparative analysis of readability and DISCERN quality across ChatGPT and deepseek models

International Journal of Medical Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 105871 - 105871

Published: March 1, 2025

Language: Английский

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Real-world radiology data for artificial intelligence-driven cancer support systems and biomarker development

ESMO Real World Data and Digital Oncology, Journal Year: 2025, Volume and Issue: 8, P. 100120 - 100120

Published: March 22, 2025

Language: Английский

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Novel Two-Stage, Contrastive Learning for Predicting the Phenotype of Metabolic Dysfunction-Associated Fatty Liver among Adults: Method Development and Evaluation (Preprint)

Published: April 9, 2025

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a leading cause of chronic and may develop into fibrosis or hepatocellular carcinoma. It has distinct subtypes—obesity, diabetic, lean—each associated with divergent burden complications. However, existing analytics methods often overlook the multisystem nature, intra-phenotype heterogeneity dynamics. These limitations hinder accurate risk stratification personalized intervention planning for MAFLD patients. This study develops novel, two-stage, contrastive learning–based method to estimate phenotype metabolic among adults. The proposed leverages multi-view learning; it models individual important relationships in clinical survey-based data make effective predictions support decision-making care. Demographic, clinical, lifestyle-related, family genetics history–oriented 4,408 adults reveal how capturing from different sources can transform individual-level representations multiple, complementary views. comparative evaluation predictive efficacy method, comparison eight prevalent methods, relies on recall, precision, F-measure, area under curve. consistently significantly outperforms all benchmark methods. attains highest F-measure value, 34.0% improvement non-diabetics 22.2% diabetes than respective best-performing benchmarks. results demonstrate value utilities integrating better phenotypes provides viable means estimates. more efficacious identifying at-risk many data-driven thereby enhance

Language: Английский

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