Artificial intelligence and deep learning techniques are rapidly transforming diagnostics and pharmaceutical workflows. Recent breakthroughs include a deep learning model accurately predicting myopia severity and gastric cancer histopathology images, along with AI frameworks assessing sinus surgery outcomes. Innovations such as self-supervised learning enhance healthcare wearable data analysis, while novel AI tools, despite concerns about hallucinated reports, are deployed for regulatory tasks. Furthermore, deep learning has enabled non-invasive glioma subtype predictions. These advances herald improved diagnostic precision and optimization of clinical trial design, although caution regarding AI-generated data quality persists.