Future Directions in Bioscience Information Systems

Abstract

Bioscience information systems -- the computational infrastructure connecting biological data generation, storage, analysis, interpretation, and clinical translation -- are undergoing a paradigm shift driven by foundation models, federated architectures, autonomous AI agents, and real-time data integration. The convergence of these technologies promises systems that autonomously discover biological knowledge, predict patient outcomes, design therapeutic interventions, and monitor treatment response in continuous closed loops. Yet realising this vision requires addressing fundamental challenges in data interoperability, computational scalability, algorithmic trustworthiness, and governance. We present the Bioscience Information Systems Futures Framework (BISFF), evaluating five emerging paradigms -- biological foundation model ecosystems, autonomous scientific discovery agents, federated biomedical data meshes, real-time digital health twins, and trustworthy AI governance platforms -- across four future-oriented assessment dimensions (technological readiness, scientific impact potential, translational feasibility, and societal benefit). Our Futures Readiness Score (FRS) measures current maturity, projected 5-year impact, implementation barriers, and governance preparedness. Biological foundation model ecosystems achieve the highest FRS (0.928) through demonstrated capability across protein, genomic, and clinical domains, while trustworthy AI governance platforms achieve the highest societal benefit score (0.960) by addressing the ethical and regulatory requirements that determine public acceptance of biomedical AI