Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
This scoping review examined 23 studies published between January 2000 and September 2025 on AI in orthodontic diagnosis, treatment planning, appliance design, and teledentistry. It found AI-assisted systems can improve diagnostic precision and reduce clinical workload, and that remote monitoring platforms can cut in-person appointments while maintaining standards and improving compliance.
Researchers introduced AMIE, an LLM-based system for diagnostic dialogue, and tested it against 20 primary care physicians in 159 text-based scenarios with patient-actors from Canada, the UK and India. Specialist and patient-actor raters scored performance across 32 and 26 axes including history-taking and management.
On 15 July 2026, Prime Minister Anthony Albanese announced a new office of AI and said Australia will legislate the strongest possible protection for creatives against unlicensed use of their work to train AI models, while also imposing strict new rules on large energy-intensive datacentres.
Published January 2024, this IEEE Access survey reviews how machine learning, deep learning and reinforcement learning are applied to cybersecurity tasks such as malware detection, intrusion detection and vulnerability assessment, including evaluation of ChatGPT-like tools on both defensive and offensive sides.
A multisite retrospective validation study in a US tertiary health system compared four automated EMR retrieval methods to manual chart adjudication for ischaemic stroke/TIA, MI, HF exacerbation/hospitalisation and composite MACE in 2258 patients treated with immune checkpoint inhibitors and 1426 patients who underwent TAVR. The zero-shot LLM workflow achieved the highest AUCs for most outcomes, while ICD-based retrieval remained competitive.
By August 13, 2026, a systematic review and meta-analysis of 18 studies found AI models predicted laparoscopic cholecystectomy difficulty with pooled AUCs of 0.848 in training and 0.818 in validation, with ensemble models reaching 0.889 and 0.861. The review searched four databases to March 2, 2026 and used PROBAST and GRADE to assess bias and certainty.
This narrative review from August 2026 summarizes AI applications across occlusion-oriented digital reconstruction of maxillofacial fractures, where treatment must address stable occlusion, mandibular movement, temporomandibular joint position, facial contour, and fixation as interdependent targets. It evaluates tasks from CT/CBCT screening and segmentation to model repair, shape completion, planning assistance, and postoperative deviation analysis.
ace