Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers compared large language models and clinicians on 34 real-world glaucoma cases, with glaucoma specialists scoring responses on medical accuracy, key-point recall, and logical completeness. AI models produced structured reasoning with weighted mean scores overlapping attending ophthalmologists and exceeding some residents.
Published October 22, 2025, this PLOS One scoping review examined literature on AI and large language models like ChatGPT in medical education. It found potential for personalized learning alongside a set of ethical challenges, synthesizing 50 studies from three major databases covering 2010 to August 2024.
A systematic review published October 14, 2025 synthesized peer-reviewed literature from January 2020 to July 2025 on AI in nutrition and dietetics, covering dietary assessment, personalized nutrition and chronic disease management, generative AI and conversational agents, public health nutrition, sensory science, and ethics.
A peer-reviewed cross-sectional study of 358 medical students from November 2025 to January 2026 examined factors linked to attitudes toward AI using online questionnaires including digital literacy and emotional intelligence scales. Most students had used AI, but majorities reported ethical or legal concerns and worries about reduced clinical reasoning.
Published March 23, 2026, this peer-reviewed integrative review examined how automation, artificial intelligence and disruptive technologies are changing human resource management. Using thematic analysis of secondary data, the authors identified four competency themes and proposed a framework intended to make HRM professionals aware of skills needed to be future-ready.
Published March 26, 2026, this peer-reviewed article revisits the Six Human-Centered AI Grand Challenges in light of generative AI. It argues that while generative systems move AI toward creative interaction, benefits depend on addressing governance gaps and new technical risks.
This 2024 peer-reviewed discussion examines how biases arise and compound throughout the medical AI lifecycle, from data features and labels through model development, evaluation, deployment, and publication, and how those biases affect clinical decision-making.
Published November 8, 2024, this review examines whether deep learning models for medical diagnosis can maintain performance when exposed to adversarial or noisy inputs, analyzing influences such as model complexity, training data quality, and hyperparameters.
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