Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
This peer-reviewed review from October 2024 surveys how AI techniques such as machine learning and deep learning are applied across pharmaceutical development, including target identification and validation, excipient selection, synthetic route prediction, supply chain optimization, and continuous manufacturing monitoring.
By July 2026, this scoping review had mapped the literature on AI in OSCEs, screening 421 records and including 22 studies across health professions education. It categorized uses in preparation, station construction, scoring, and delivery, and stratified findings by evidence maturity using FACETS, SAMR, and P4 frameworks.
By March 2026, this peer-reviewed overview described how AI-based automation in Industry 4.0 optimized production, logistics, and resource management to reduce waste and energy use, and how Industry 5.0 expanded that with human-machine collaboration, generative AI, digital twins, and decentralized smart grids and microgrids.
On 2021-10-28 a peer-reviewed survey in The Innovation reviewed how artificial intelligence coupled with machine learning is being developed and applied across information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry.
A peer-reviewed study in Journal of Health Organization and Management examined how healthcare workers respond to AI-driven transformation. Using face-to-face surveys of 305 staff at a university hospital in Istanbul in early 2026, the authors tested whether openness to organizational change and attitudes toward AI affect innovative work behavior via technostress.
Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases.
Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs. Artificial intelligence (AI)-assisted fracture detection tools are now available for use in radiology workflows; however, the impact of these technologies on patient outcomes, experiences and overall care pathways in the real-world clinical setting is limited.
To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. In this prospective, monocentric, crossover reader study, five readers (one to six years of experience) assessed 1200 consecutive patients undergoing chest radiography (1861 total radiographs) for pulmonary infiltrates, pleural effusions, mediastinal masses, pneumothorax, and pulmonary nodules under five conditions: without AI and with each of four AI algorithms.
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