arXiv:2607. 18884v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes.
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arXiv:2607. 09253v1 Announce Type: cross Abstract: AI chatbots are increasingly used for answering health-related questions.
The study surveyed 761 Nigerian healthcare professionals to assess their readiness for AI adoption in clinical settings. While 92.6% were aware of AI, only 63.0% felt prepared and 40.9% reported low knowledge, highlighting a gap between awareness and readiness. Major barriers identified were lack of training, poor infrastructure, high costs, job displacement fears, ethical and data privacy concerns, with significant regional and professional differences in preparedness and attitudes.
arXiv:2602.11391v5 Announce Type: replace Abstract: Objective: This study develops and validates a patient simulation framework that aligns with the National Institute of Standards and Technology AI...
arXiv:2606. 20605v2 Announce Type: replace-cross Abstract: Background: Generative artificial intelligence (GenAI) is increasingly used for health information, yet its influence on users' trust calibration remains unclear.
arXiv:2606. 08483v1 Announce Type: new Abstract: Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather than retrieve them.
The study compares communication styles of large language models (LLMs) and humans in explaining COVID‑19 misinformation, using a dataset of 1,498 fact‑checking claims and 99 blinded reader evaluations. LLM‑generated explanations scored lower on persuasive strategies, certainty, and alignment with social values, yet over 60% of participants preferred LLM content for clarity, completeness, and persuasiveness. The findings suggest that reader preference may not align with traditional measures of communication quality, highlighting both the promise and limits of LLMs in health communication.