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5 emerging AI/ML trends in clinical research
Emerging AI and machine learning tools are reshaping clinical research as trial sponsors continue to grapple with rising costs, lengthy timelines and low success rates.
As clinical designs become more complex and data volume grows, the recruitment and retention of trial participants remain among the biggest hurdles for sponsors, opening the door for AI-powered tools.
Some researchers are turning to artificial intelligence and machine learning (ML) to address these challenges and improve operational functions. These tools can be used to point out the most promising trial sites, increase enrollment by up to 20% and create real-time enrollment forecasts, enabling earlier, more proactive interventions.
Deploying AI/ML technology can also bring innovative therapies to market more quickly -- cutting development timelines by an average of 6 months per asset. A 12-month reduction in clinical development, for example, can boost a sponsor’s portfolio by more than $400 million in net present value, research shows.
Here's a look at five ways emerging AI and ML tools are being used in clinical research, according to WCG's 2026 Trends & Insights report.
Optimizing site selection with predictive analytics
Predictive analytics is changing the way trial sponsors and CROs approach protocol design and site selection strategies. Drawing on historical data, comparable protocol performance and demographic and epidemiological trends, predictive models can pinpoint regions and site profiles most likely to yield optimal participant populations.
This data-driven approach, powered by predictive algorithms, analyzes vast amounts of data to meet study needs and potentially lower dropout rates, making it one of the most impactful AI innovations in the industry, the report highlights.
It also empowers teams to make more informed decisions earlier in the trial lifecycle, which can minimize costly mid-study adjustments and protocol changes.
Leaning into proactive trial monitoring with machine learning
The clinical research industry is starting to shift away from reactive problem-solving and lean into proactive risk management, with the help of machine learning.
By drawing on models trained on large datasets, machine learning allows study teams to identify emerging risks earlier in the process and address potential bottlenecks before they disrupt trial timelines.
Machine learning also helps sponsors and CROs better allocate resources, proactively address site performance issues and sustain momentum throughout the study, which ultimately improves trial execution speed and quality.
Compared to conventional monitoring techniques, which frequently discover issues after they have already affected deadlines or data quality, this predictive capability is considered a major advancement, the report suggests.
Advancing protocol development with generative AI
Large language models and generative AI are expanding the role of augmented intelligence to clinical trial document creation and protocol review processes, WCG's report indicates.
These AI applications can quickly generate research documents like informed consent forms and trial protocols, perform pre-reviews to make sure the right regulatory components are included and even power AI bots that allow research staff to ask specific questions about protocols. This level of automation lets clinical teams focus on higher-value work rather than spend time on tedious tasks like document drafting, improving efficiency and consistency.
They can also help with more complex tasks such as protocol evaluations and regulatory compliance checks, but human oversight is still needed to make sure expert judgment remains part of the review process.
Improving data quality with AI-powered anomaly detection
AI-driven anomaly detection is transforming data quality management in clinical trials by minimizing manual data entry, reducing errors and offering real-time oversight of data integrity. Instead of finding inconsistencies, outliers and possible problems with data quality later on during data review, integrated platforms and smart data systems with automated compliance checks can spot them as they crop up.
This capability is especially valuable as trial designs become more sophisticated and datasets grow larger, which makes manual oversight difficult and time-consuming.
AI anomaly detection has operational benefits that extend throughout the review phase of regulatory submissions and ongoing study conduct. While ongoing monitoring throughout studies generates feedback loops that facilitate quicker decision-making and course corrections, real-time feedback systems expedite regulatory submissions by identifying errors before documents are submitted, thereby saving time and improving research integrity.
Transforming patient recruitment and engagement with AI
The way sponsors and sites find, enlist and interact with clinical trial participants over the course of a study is being reshaped by AI systems. By evaluating medical records to determine a patient's eligibility based on inclusion and exclusion criteria, AI tools can significantly reduce prescreening time and improve recruitment accuracy.
AI is also being used to keep electronic diaries, gather patient-reported outcomes and support continuous participant engagement -- all of which streamline data collection while lessening the workload for site personnel and trial participants.
However, certain ethical and legal implications must be carefully considered when deploying AI in participant-facing applications, as confidentiality and privacy still remain major concerns for participants, particularly when private health data is involved.
As AI technologies continue to advance, strong governance frameworks and human oversight are essential to uphold ethical standards and ensure patient safety.
Alivia Kaylor is a scientist and the senior site editor of Pharma Life Sciences.
