AI-enabled clinical decision support systems will gain traction as trust improves, data interoperability strengthens, and targeted investments in AI-driven diagnostics and personalized therapy recommendations accelerate adoption.

Readers are invited to register for a SAS webinar on Top Health and Life Sciences Trends for 2026 to explore these predictions in depth.

Quantum machine learning is poised to advance predictive toxicology in preclinical drug development by more accurately simulating quantum effects, enabling earlier safety flags and reducing early-stage and late-stage failures.

Data orchestration will harmonize multimodal data streams—from digital biomarkers, genomics, imaging, and labs—enabling robust analyses and tighter ties between discovery and clinical workflows.

Copilots and AI agents will automate tasks and speed drug submissions, with human validation; governance in Europe under the EU AI Act will influence ownership and deployment of AI-driven processes.

AI will enhance pharmaceutical manufacturing through digital twins, predictive maintenance, real-time process monitoring, automated quality assurance, and blockchain-based traceability for compliance.

SAS’s 2026 predictions frame health care and life sciences as a steady evolution where data and AI form core infrastructure across clinical, operational, and manufacturing domains.

In-home and remote care growth will rely on IoT, event processing, and AI-enabled insights to support hospital-at-home models and manage chronic conditions more cost-effectively.

AI productivity stacks will become standard in enterprises, integrating deterministic AI with large language models to support end-to-end operations, including medical billing and other workflows.

AI will drive personalized medicine by analyzing genomics, patient history, and treatment data to guide therapies and trial participation, while aiding drug discovery and toxicity prediction.

Regulatory sandboxes using synthetic clinical data will accelerate AI validation, trial simulations, and decision-support tool testing while safeguarding privacy and regulatory compliance.

Multimodal real-world data will become standard for evidence generation, integrating EMRs, imaging, wearables, patient-reported outcomes, genomics, and social determinants to improve interoperability and data standardization.