AI in health care is entering a phase of steady, strategic evolution in 2026, according to predictions published by SAS, a global data and analytics company, on April 2, 2026. The forecast covers 15 distinct trends spanning clinical decisions, drug discovery, rural care, pharmaceutical manufacturing, and data governance.
SAS experts said leading organizations will treat data and AI as core infrastructure rather than experimental tools. The predictions draw on insights from more than a dozen health care and life sciences advisors across the company’s global teams.
Data Orchestration and Multimodal Analysis
Dr. Mark Lambrecht, Global Head of Health Care and Life Sciences at SAS, said life sciences organizations will move beyond isolated data points in 2026. Continuous data streams from digital biomarkers, genomics, imaging, and clinical laboratories will require robust data engineering to harmonize complex signals. Lambrecht said significant investment is expected in joining discovery and clinical analytical data fields.
William Kuan, Health Care and Life Sciences Strategic Advisor at SAS, said multimodal real-world data is rapidly becoming standard practice in evidence generation. Large language models, he said, will help solve decades-old interoperability challenges by accelerating data standardization across structured records, clinical notes, medical imaging, wearables, genomics, and social determinants of health.
AI in Health Care: Clinical Decisions and Personalized Medicine
Dr. Mark Wolff, Health Care and Life Sciences Strategic Advisor, said 2026 will see accelerated adoption of AI-enabled clinical decision support systems. He attributed this shift to growing clinical trust, improved data interoperability, and strategic investments highlighted in recent industry analyses.
Pritesh Desai, Life Sciences Strategic Advisor, said AI models will analyze patient genomics, medical history, and treatment data to recommend therapies or clinical trial participation. He added that AI-driven molecular interaction modeling and drug candidate screening will reduce time and cost in early-stage drug discovery.
“The success of AI doesn’t depend on the algorithms alone, it depends on the data that fuels them.”
Grace Gu, Health Care Strategic Advisor, SAS
Quantum Computing, Rural Care, and Home Health
Brittany Shriver, Head of Global Life Sciences Strategic Advisory, said quantum machine learning will be applied to predictive toxicology for novel drug candidates in 2026. By simulating complex quantum mechanical effects, these models will flag safety issues earlier than classical AI, substantially reducing preclinical research failure rates.
Amanda Barefoot, Head of Global Health Care and Life Sciences Strategic Advisory, said AI will become the main driver of rural health access. Virtual agents will handle triage, care navigation, and ongoing monitoring, while hybrid care teams use AI tools to interpret diagnostics and guide clinicians. Heather Hallett, RN, Head of US Health Care Strategic Advisory, said home health spending is expected to rise as hospital-at-home programs gain momentum, with IoT devices and event stream processing delivering real-time insights for chronic condition management.
Regulatory Sandboxes, Pharma Manufacturing, and Sustainability
Christian Hardahl, Head of EMEA Health Care Strategic Advisory, said hospitals, health organizations, and startups will use regulatory-approved sandboxes with synthetic clinical data to test AI models and simulate clinical trials without breaching privacy laws. Meanwhile, Sharon Napier, Life Sciences Strategic Advisor, said the pharmaceutical supply chain will become more digitally integrated, with AI supporting predictive maintenance, real-time process monitoring, and automated quality assurance. Digital twins and blockchain will also be used for simulation and traceability.
Lisa Murch, Life Sciences Strategic Advisor, said a meaningful shift is expected in health care sustainability, moving from data collection and reporting to practical AI-driven changes in optimization and predictive logistics. She added that the environmental impact of AI will face greater scrutiny and will begin influencing procurement decisions.
Heather Trimble, Health Care Strategic Advisor, said every major enterprise will have an AI productivity stack by the end of 2026. She said generative AI attracts attention, but deterministic AI drives operational outcomes. Robert Collins, Health Care and Life Sciences Strategic Advisor, said investments in 2026 will focus on ensuring AI benefits while limiting risks to privacy and data misuse that accompany health care digitalization.
