BioMedInformatics

Scientific evidence is fundamental to uncovering insights about health, including food and nutritional claims. Substantiating such claims requires robust scientific procedures that often include clinical studies, biochemical analyses, and the examination of multiple forms of data. The growing capabilities of artificial intelligence (AI) and large language models (LLMs) present new opportunities for analyzing food–health relationships and supporting health claim validation. Yet, applying these technologies to the food and nutrition domain raises challenges that differ from those encountered in broader biomedical text mining (TM). In this perspective, we review key issues, including the complexity and heterogeneity of food-related data, the scarcity of food-specific language models and standardized resources, difficulties in interpreting nuanced and often contradictory evidence, and requirements for integrating AI tools into regulatory workflows. We compare modern LLM approaches with traditional TM methods and discuss how each may complement the other. Our position is that, despite their promise, current AI and LLM tools cannot yet reliably handle the subtleties of food–health evidence without substantial domain-specific refinement and human expert oversight. We advocate for hybrid approaches that combine the precision of established TM techniques with the analytical breadth of LLMs, supported by harmonized ontologies, multidimensional evaluation frameworks, and human-in-the-loop validation, particularly in regulatory contexts. We also highlight the importance of public education, transparent communication standards, and coordinated cross-disciplinary efforts to ensure these technologies serve broader goals of food safety, consumer trust, and global health.

Conventional topical therapy for corneal and anterior segment diseases is limited by rapid tear clearance and multilayer corneal barriers, resulting in low bioavailability and the need for frequent dosing. Artificial intelligence (AI) is emerging as a complementary approach that learns quantitative relationships between molecular structure, formulation variables, and ocular performance. In corneal drug delivery, machine learning models have been used to optimize multicomponent formulations and processing conditions; predict key quality attributes such as particle size, zeta potential, encapsulation efficiency and release kinetics; and estimate corneal permeability, retention and ocular irritation risk, thereby reducing experimental burden and guiding safer design. AI can also be coupled with mechanistic ocular pharmacokinetic/pharmacodynamic models to translate formulation attributes into predicted tissue exposure. Finally, inverse design approaches enable the discovery of new carriers and devices, illustrated by machine learning-guided peptide carriers and smart contact lens platforms that combine sensing with on-demand drug release. Despite these advances, current datasets remain small and heterogeneous, external validation and benchmarking against conventional workflows are limited, and uncertainty quantification and interpretability must be addressed to enable clinical translation. This review summarizes corneal barriers and delivery platforms, critically evaluates where AI provides measurable value across design, characterization and performance and highlights data and validation priorities needed for trustworthy AI-enabled corneal therapeutics.

BioMedInformatics is an international, peer-reviewed, open access journal that covers all areas of biomedical informatics, computational biology, and medicine. Established in 2021, the journal is now five years old and reflects the evolution of the field through its consistent thematic focus on Artificial Intelligence (AI)-driven diagnosis and prediction, with a particular emphasis on translational clinical decision support and biomedical signal and imaging analysis. Despite the predominance of AI-related topics, classical bioinformatics remains a major focus, with a particular emphasis on the discovery of biomarkers and the development of data resources. This editorial summarises this evolution, which accurately reflects the field as a whole.