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.
By Milad Yousefi, Hadi Vahedi, Shadi Farabi Maleki, Mahya Ahmadpour Youshanlui, Aida Jafari, Parisa Rostami, Kais I. Abdul-Lateef Al-Abdullah, Ryszard Tadeusiewicz, Pawel Plawiak, Roohallah Alizadehsani, Siamak Pedrammehr
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.
By Md. Sadibul Hasan Sadib, Md. Mohayminul Mukit, Rahmatul Kabir Rasel Sarker, Tahmid Alam Tamim, Md. Monir Hossain Shimul
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.
By Hai Pham Ngoc
arXiv:2606. 19174v1 Announce Type: cross Abstract: Clinician-centered evaluation is critical for validating medical AI systems, especially in ultrasound imaging where quantitative metrics do not always capture clinical usability.
By Fangyijie Wang, Jianjun Yu, Wentao Shi, Haixia Huang, Ran Shi, Gu\'enol\'e Silvestre, Kathleen M. Curran
arXiv:2608.28820v1 Announce Type: new
Abstract: Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and...
By Shubham Innani, Suhang You, Adam Shephard, Bhakti Baheti, Francesco Ciompi, Joe Yeong, Nasir Rajpoot, Michael Feldman, Solene Florence Kammerer-Jacquet, Dimitrios Makris, Geert Litjens, Anne L. Martel, Jana Lipkova, April Khademi, Spyridon Bakas, for the MICCAI SIG-CompPath
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:2606. 11830v1 Announce Type: new Abstract: Background.
By Qianyu Yao, Fei Sun, Bocheng Huang, Wei Chen, Jiarui Jiang, Shu Quan, Yifei Chen, Wenjie Xu, Bo li, Liping Su, Ruoqiong Wu, Huhai Hong, Huimei Wang
A specialized large multimodal model, LLaVA‑NeXT, was fine‑tuned on a curated two‑level curriculum of PET/CT image‑conversation pairs to interpret head and neck cancer scans. In external validation across four institutions, the model achieved high ROUGE and similarity scores and outperformed generalist models such as ChatGPT, with primary tumor classification accuracy of 83.14% internally and 69.03% externally. The study demonstrates that domain‑specific LMMs can provide fast, accurate diagnostic support for PET/CT imaging.
By Haengbok Chung, SunGyu Kim, Joo hyun Lee, Sangjin Bae, Min Jeong Cho, Minseok Suh, Jae Sung Lee
The study introduces MitPro, an AI tool that guides pathologists to regions with high mitotic activity and highlights mitotic figures, aiming to reduce variability and time in tumour grading. In a retrospective paired reader study of 385 whole‑slide images across seven tumour types, AI assistance raised the intraclass correlation coefficient from 0.589 to 0.949 and cut median assessment time from 286.4 to 127.8 seconds. The tool also produced a slight increase in mitotic counts, aligning with the detection of more active hotspots, while the frequency of score changes matched typical inter‑pathologist variation.
By Simon Graham, Mostafa Jahanifar, Quoc Dang Vu, Vygante Maskoliunaite, Donatas Petroska, Ruta Barbora Valkiuniene, Ayat Gamal Lashen, Jen Hong Ong, Amede Ogechi Nnorom, Sinclair Couper, Natasha Kardasz, Reshma Agrawal, Brinder Singh Chohan, Jose Luis Solorzano Rendon, Shonali Natu, Arvydas Laurinavicius, Nasir Rajpoot, David Snead
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.
By Marie-Lisa Eich, Kai Standvoss, Timo Milbich, Alexander M\"ollers, Miriam H\"agele, Philipp Anders, Lars Tharun, Hanna Kontradiuk, Sebastian Kons, Nader Aldoj, Recepcan Adig\"uzel, Adam Narai, Lukas H\"onig, Jonathan Striebel, Binru Yang, Mihnea P. Dragomir, Marvin Sextro, Philipp Keyl, Philipp Jurmeister, Rosemarie Krupar, Evelyn Ramberger, James Wells, Julika Ribbat-Idel, Andreas Kunft, Hussam Shuaib, Christian Groh\'e, Reinhard B\"uttner, David Horst, Klaus-Robert M\"uller, Lukas Ruff, Maximilian Alber, Frederick Klauschen, Simon Schallenberg
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).
By Bingxin Yu, Xueli Wang, Jerry Zhou, Wenyan Wang, Li Wen, Lan Huang, Xin Feng, Fengfeng Zhou, Kewei Li
arXiv:2608.22108v1 Announce Type: new
Abstract: Breast Cancer Multidisciplinary Team (MDT) meetings manage increasingly complex cases under considerable time pressure, and documentation requirements...
By Aarzoo Dhiman, Farzana Haque, Kartikae Grover, Lydia Brian Smith, William Stephen Jones