Oracle uses AI agents and real-world data analytics to strengthen its life sciences drive

Oracle has announced new enhancements for its life sciences AI data platform that include AI agents pre-trained with the domain knowledge of the industry and improved analytics and natural-language data exploration tools that will speed clinical research.

Evolution of Life Sciences Data Intelligence

In January, the company introduced its Oracle Life Sciences AI Data Platform as the basis for a data platform for life sciences. The platform includes “agentic reasoning, generative AI, and customizable AI agents, which enable pharmaceutical, medical device, and research companies to speed up drug development, clinical trials, safety monitoring, and commercialisation processes,” the company said.

The company has subsequently added capabilities for life sciences workflows powered by AI and real-world data to the platform, which is now known as Life Sciences Data Intelligence. These capabilities can be leveraged to provide research teams with evidence, earlier detection of patients in disease progression, and to assist clinical research initiatives, the company claims.

The platform improvements are part of Oracle’s wider life sciences plan, which was announced at the Oracle Health and Life Sciences Summit 2026.

Integrating Real-World Data at Scale

Oracle states the Life Sciences Data Intelligence platform integrates a customer’s data with Oracle’s repository of real-world data, such as more than 122 million longitudinal health records. The solution integrates these data sources into a single secure platform, while Oracle’s robust oversight controls transform de-identified patient data into insights that enable organizations to take action, Oracle executives said. The platform will also be designed to accept more third-party data in the future.

The cloud-native platform also provides flexibility for organizations to grow with their data volume, research needs, and AI use cases as they go, the company said.

Fragmented data and disconnected workflows remain a hindrance on the journey to discovery, said Seema Verma, executive vice president and general manager, Oracle Health and Life Sciences, in a statement. “Oracle’s unique ability to offer real-world data, along with domain-specific AI tools, helps enable researchers to conduct studies and explore data in natural language, accelerating research from discovery to commercialization.”

Domain-Trained AI and Automated Workflows

With Oracle domain-trained AI capabilities, life sciences researchers can analyze data, create and further customize patient cohorts and automate complex research workflows with natural-language queries, without the need for extensive coding. They include features powered by AI that support researchers in a variety of common tasks such as cohort discovery, clinical trial recruitment, site optimization, health economics & outcomes, market access research & evidence generation. Moreover, the tools give traceable reasoning and transparent audit trails to assist users in comprehending how results were produced and to validate the underlying evidence, the company has stated.

Advanced analytics go beyond basic analytics tools, offering executives contextual guidance for analysis and interpretation, they said. The Connected intelligence workflows also tie models, cohorts and insights together to ensure consistency and repeatable outcomes throughout the enterprise.

“Oracle is addressing the ‘trust gap’ for researchers by ‘providing governed real-world data along with domain-trained AI capabilities and sophisticated analytics within a single connected environment that answers complex research questions with credibility in record time,’” said Nimita Limaye, research vice president, life sciences R&D strategy and technology at IDC.

Supporting Clinical Research

Real-world data can provide valuable information about patients outside traditional clinical trial settings. Researchers can use this information to study treatment patterns, patient populations, outcomes and healthcare utilization.

These capabilities may also support clinical trial planning. Identifying appropriate patient populations and understanding characteristics of potential participants can help research teams evaluate study feasibility and improve recruitment strategies.

Improving Evidence Generation

Healthcare organizations increasingly need evidence that extends beyond controlled clinical trials. Real-world evidence can contribute to understanding how treatments perform across broader patient populations and routine healthcare environments.

Advanced analytics can help researchers identify patterns across large datasets while providing tools for filtering populations and comparing outcomes. Strong governance and transparent analytical methods remain important because healthcare datasets can contain missing information, inconsistencies and potential sources of bias.

AI-Powered Research Workflows

Specialized AI agents can automate parts of analytical workflows by interpreting research questions, selecting appropriate data resources and producing preliminary analyses. Human researchers can then review the results, validate assumptions and determine whether findings are appropriate for scientific or business decisions.

Oracle is expanding its presence in the life sciences sector by combining domain-trained AI agents, real-world data and advanced analytics through its Oracle Life Sciences Data Intelligence platform.

The growing use of artificial intelligence is changing how pharmaceutical and biotechnology organizations manage research data. Modern platforms can bring together clinical, genomic, patient, claims and other healthcare information, allowing researchers to examine large datasets through centralized analytical environments.

AI-powered research tools can reduce some manual work involved in cohort identification, data exploration and evidence generation. Instead of building every analysis from scratch, researchers can interact with specialized systems using natural-language questions and receive structured outputs such as patient counts, tables, charts and summaries.

Oracle and the Future of Life Sciences AI

The Oracle expansion reflects a broader movement toward using AI to support scientific and healthcare workflows. By combining real-world data with domain-specific AI agents and advanced analytics, Oracle is developing tools intended to help research teams move from questions to evidence more efficiently.