Correction: Editorial: Digital medicine and artificial intelligence
• Please read through all the templates before choosing • Pick the most relevant text template(s) from the following page and delete all others. • Edit the text as necessary, ensuring that the original incorrect text is included for the record, please see the below. • Please do not use any extra formatting when editing the templates, and only modify the red text unless absolutely necessary • Submit to Frontiers following the instructions on this page.When the original text contained incorrect information, to preserve the scientific record, please include that text when editing the below templates. For example:There was a mistake in the Funding statement, an incorrect number was used. The correct number is "2015C03Bd051.". The publisher apologizes for this mistake.The original version of this article has been updated. 2026). Are Müller glial cells gatekeepers of neuroprotection and regeneration in age-related macular degeneration? Unraveling their roles in pathophysiology and therapeutic innovation. Prog. Retin. Eye Res. 112:101471. doi: 10.1016/j.preteyeres.2026.101471" will be removed. The paragraph will now read: "The contributions collected in this Research Topic collectively indicate that the next phase of digital medicine should focus less on algorithmic novelty and more on generating clinically actionable evidence that supports routine healthcare implementation (Figure 1). Although the included studies demonstrate substantial advances across AI-enabled diagnostics, digital therapeutics, medical imaging, large language models, implementation science, and healthcare informatics, they also reveal several common priorities that should guide future research. Building upon these advances, future innovation is expected to further benefit from emerging technologies that complement the themes represented in this Research Topic, including mobile health (mHealth) (Wang et al., 2025), LLMs, generative artificial intelligence (Generative AI), wearable sensing systems (Song et al., 2026), federated learning (Fu et al., 2026), and multimodal foundation models (Wang et al., 2024b). However, their successful translation into clinical practice will depend not only on technological innovation but also on rigorous prospective validation, demonstrated clinical utility, seamless workflow integration, fairness, privacy-preserving learning, LLM safety, benchmark standardization, and appropriate regulatory oversight."The original version of this article has been updated.