AI-driven techniques for thoracic disease classification in chest radiography: A comprehensive review and critical analysis

Foundation Models in Medical Imaging: A Systematic Review, Taxonomy, and Future Research Agenda

This systematic review evaluated forty-two studies published between 2024 and 2026 across eleven databases using PRISMA 2020 guidelines and AI-specific quality frameworks and demonstrated high organ segmentation accuracy with Dice coefficients exceeding 0.90, whereas performance declined for infiltrative tumours, and e...

A comprehensive review of AI-based approaches for lung disease classification using chest X-ray images

The Promise and Pitfalls: A Systematic Review of AI-Powered Diagnostics in Modern Oncology

Purpose: Despite the rapid expansion of AI-powered diagnostics in oncology, most existing literature reports algorithm performance in isolated, retrospective settings, and no recent systematic review has jointly synthesised algorithm types, validation rigor, implementation barriers, and patient-outcome evidence. This l...

A Critical Methodological Review of Clinical Scores, Machine Learning, and Large Language Models for the Diagnosis and Management of Pediatric Appendicitis

Background/Objectives: Acute appendicitis is the most common surgical emergency of childhood, yet its diagnosis remains difficult because presentations are atypical, inflammatory markers are nonspecific, and the consequences of error run in both directions, from negative appendectomy to missed perforation. Over the pas...

Performance vs Consistency: Evaluating a Foundation Model in Lung-RADS Screening

MedGemma, a medical general-purpose foundation model derived from Gemini and its fine-tuned version adapted for lung cancer detection and diagnosis is compared against radiologists performing Lung-RADS v2022 assessment on the NLST dataset, supporting the role of fine-tuned foundation models potential complementary tool...

Advancements toward clinical application of AI-assisted CT analysis in pulmonary hypertension: a systematic literature review.

AI-assisted CT analysis shows potential for advancing pulmonary hypertension assessment through automated quantitative biomarkers, but robust validation, methodological comparability, and clinically intuitive metrics are required before potential clinical integration.