This review examines the use of artificial intelligence and radiomics for diagnosing thyroid cancer, summarizing 42 studies that demonstrate the effectiveness of ultrasound-based radiomics in identifying malignancies. It highlights challenges such as interpretability, limited datasets, and operator dependence, and calls for standardization, prospective multicenter trials, and advances in explainable AI and personalized medicine. The authors emphasize that multidisciplinary collaboration and further algorithm refinement could enhance diagnostic accuracy and patient outcomes.
arXiv:2608. 12590v1 Announce Type: new Abstract: Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratification and reporting, yet most AI systems address these tasks in isolation and provide limited support for clinical review.
arXiv:2607. 12075v1 Announce Type: cross Abstract: Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabilities, uncertainty estimation, and selective referral, particularly under dataset shift.
arXiv:2609.16597v1 Announce Type: cross Abstract: Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapp...
arXiv:2608.28820v1 Announce Type: new Abstract: Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and...
Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapping imaging features across tumor types, inter-obse...
LUCAID is an agentic multimodal AI system designed for precision lung cancer pathology, integrating nine modules that cover the entire routine workflow—from quality control and tumor detection to histological subtyping, microenvironment profiling, cellularity quantification, and biomarker scoring (PD‑L1, MET, TROP‑2). The system generates automated structured reports and allows interactive querying of module outputs. In prospective clinical validation, LUCAID achieved 93.0% concordance with an expert‑panel reference standard for clinically actionable decisions, outperforming five experienced thoracic pathologists who ranged from 68.3% to 81.1% concordance.
Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Yet pathological asses...
arXiv:2608. 13939v1 Announce Type: cross Abstract: Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are aggregated into five risk levels (TR1-TR5).
The paper introduces MedDream, a radiographic world model that learns a shared continuous latent state from paired chest radiograph-text observations. MedDream outperforms existing diagnostic and generative AI models across eight clinical datasets, improving diagnostic reasoning, resident concordance, and evidence generation. Targeted synthetic augmentation guided by subgroup performance gaps further enhances model performance, particularly for Asian patients.
arXiv:2606. 06869v1 Announce Type: new Abstract: Aim: Existing AI-assisted traditional Chinese medicine diagnostic tools suffer from opaque reasoning processes, passive interaction, and limited treatment plan presentation.
arXiv:2609.26578v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced CT remains clinically challenging because clear cell...
TAM-Chain is a multi‑scale thyroid cytology classification framework that uses Absorbing Markov Chains and Shannon Entropy to quantify uncertainty and dynamically decide when to stop processing and refer to a specialist. It processes images at 10×, 20×, and 40× magnifications, achieving a Macro F1 score of 0.9741 on an internal test set with a 0 % false‑negative rate, and maintains a Macro F1 of 0.7026 on an external validation set with severe domain shift. The method outperforms single‑magnification baselines by adaptively adjusting stopping steps and triggering specialist referrals, thereby reducing critical diagnostic errors.