Analytics-Ready Data Powers AI Success in Clinical Research

The growing medical, logistical, and regulatory complexities of running a clinical trial are having unsustainable real-world consequences. As clinical research  costs and timelines continue to expand, patients are ultimately the ones who suffer without access to cutting-edge new therapies. Additional pressure from regulators, investors, and the public to quickly demonstrate safety and efficacy are making it even more important to accelerate the clinical pathway.

There are multiple opportunities to make clinical trials more efficient and safer, and artificial intelligence (AI) has become an exciting tool to begin addressing common bottlenecks. One promising use of AI in clinical research is to ensure data quality and patient safety.  For example, large language models (LLM) excel at providing a plain language method for clinical study teams to answer complex questions across many data sets.  Specifically, it allows Medical Monitors to rapidly analyze patient profiles and Clinical Data Managers the ability to generate custom listings using natural language.  In both cases this enables risk identification which gives sponsors the opportunity to adapt their trials and/or redesign into new studies.

Much of the talk about AI in clinical research focuses on the algorithms or the potential outputs, but nothing has more impact than accessing the right data. To leverage AI, large volumes of analytics-ready data must be available.

That requires a centralized clinical data science platform that can ensure quick access to complete, harmonized data. Efficient and comprehensive clinical data access with a solution like Signals™ Clinical, a SaaS clinical data science platform from Revvity Signals, is critical to enable AI to derive the insights that can drive improvements to your clinical trials.

AI in Clinical Research
There are many high-impact opportunities for AI in clinical trials, but here are five of the most crucial – achievable with access to the right data:

1. Automating Clinical Data Review & Reconciliation. Reviewing clinical data can be a time consuming, manual process, delaying progress and prone to human error. AI can streamline this by automatically generating custom listings for clinical data review. Rather than sifting through static tables, AI can dynamically pull relevant data points. This is a quick way to flag inconsistencies, identify outliers and errors, and improve data accuracy all while reducing manual effort and shorten cycle timelines.
2. Medical Review Automation. Medical reviewers spend countless hours reviewing patient narratives, lab values, adverse events, and safety data. AI can automate this process:
   1. Summarizing participant histories and adverse events
   2. Highlighting medically significant trends across participants
   3. Detecting patterns that might suggest safety signals
3. AI-Driven Safety Visualizations. Traditional safety data reviews rely on static outputs that frequently fail to show the bigger picture. AI can generate interactive safety visualizations that update in real-time as new data is entered.
4. Automapping Clinical Data. One of the more tedious aspects of clinical trials is the mapping of raw clinical data to standardize formats. AI can learn from previous mappings and automate these tasks, allowing for consistency across data sets and reducing manual efforts required for data standardization.
5. Clinical Trial Design. AI can augment the decision-making that connects patients to drugs. This can mean using historical data to match patient subpopulations to specific drug combinations. Conversely, some of the most powerful uses of AI in clinical development are to identify the right target patient population for a drug, refine inclusion and exclusion criteria, and better characterize enrollees. Doing so requires centralized access to uniform real-world data sets.
6. Real-Time Data Management and Analysis. Another application of AI in clinical development is addressing the challenge of real-time site monitoring, and machine learning is of particular value for the detection of adverse event under-reporting at trial sites. Platforms like Spotfire™ use machine learning algorithms that can analyze trial data to help identify the sites at risk for adverse event underreporting and position trial sponsors to respond accordingly.
7. Operational efficiency. AI accelerates clinical trials by improving efficiency, and it starts by consolidating data onto a single platform. With access to organized trial data, for example, AI can optimize site selection. By leveraging real-world data, it can support protocol design. AI in clinical research can also improve trial logistics.
8. Trial feasibility. Through analysis of historical and ongoing clinical trial data, AI can support sponsors and CROs as they review trial feasibility, presenting scenarios at the start of a study and updating assessments mid-study as well. This allows sponsors to quickly see if timelines are starting to shift - and adjust budgets and expectations accordingly.

Overcoming Obstacles for AI in Clinical Research
As mentioned earlier, in order to begin using AI in any of these areas, data must be accessible to analytical algorithms. Today, much of what a company might wish to leverage is complex data spread across multiple disparate data sets.

To be most valuable, clinical data should be analyzed as close to real time as possible. Otherwise, risks increase for patients including the potential for missed safety signals, as well as wasted time and increased costs. There are numerous unforeseen adverse impacts to a study when access to data is delayed, or when analytical outcomes are delivered too late to inform clinical decisions.

Through an end-to-end clinical data science solution like Signals™ Clinical, data can become more accessible for analytics, more quickly. It prevents data access delays and enables AI to deliver faster, more robust insights. With proper visualization tools, it becomes possible to get a holistic view of all trial data. It becomes easier to standardize and harmonize data, identify gaps in adverse event reporting from trial sites, and realize value from AI in clinical development.