The increased availability of biomedical data, particularly in the public domain, ofers the opportunity to better understand human health and to develop efective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on how data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent eforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
CEUR building trustworthy and easy-to-deploy AI models in biomedicine.
SeWebMeDa-2024: 7th International Workshop on Semantic Web solutions for large-scale biomedical data analytics, CEUR