Clinical trials are essential for developing new medicines and evaluating whether treatments are safe and effective. However, designing successful clinical studies has become increasingly complex due to rising development costs, specialised patient populations, regulatory expectations, and operational challenges.

Many clinical trials experience delays because of difficulties such as slow patient recruitment, restrictive eligibility criteria, protocol amendments, and challenges in selecting suitable study sites. These problems often begin during the early design phase, where decisions about patient populations, endpoints, recruitment strategies, and trial feasibility can determine the success of the study.

Artificial intelligence (AI) and machine learning (ML) are methods that can help research teams analyse data during clinical trial planning. AI refers to computer systems that perform tasks usually associated with human reasoning, such as identifying patterns or making predictions. ML is a branch of AI in which models learn from data rather than being programmed with fixed rules. Natural language processing, or NLP, is a form of AI used to analyse text, such as clinical notes, protocols, or scientific literature. By analysing large and complex datasets, AI can identify patterns, predict potential challenges, and provide insights that help researchers design more efficient and patient-focused studies.

In practice, the value of AI in clinical trial design depends on the decision being informed, the quality of the data used, and the controls around how the output is reviewed.

• AI in clinical trial design means using AI, ML, and NLP to support specific study decisions rather than replace expert judgement.

• It matters because early choices around eligibility, endpoints, recruitment, and feasibility can affect whether a study is deliverable.
• AI can support protocol feasibility, patient selection, recruitment planning, retrospective analysis, trial conduct, and data quality review.
• Its value depends on fit-for-purpose data, validation, human oversight, and clear controls around how outputs are used.
• The main takeaway is that AI is most useful when it answers a defined study question and improves a real design or operational decision.

The success of a clinical trial is often determined before the first patient is enrolled. A well designed trial must ensure that:

• the appropriate patient population is selected
• meaningful clinical endpoints are measured
• recruitment targets are realistic
• study sites have the required capabilities
• data collected can support reliable conclusions

Poor design decisions can result in recruitment delays, increased costs, and protocol modifications that affect study timelines.

AI can support several parts of clinical study design, including patient selection, enrichment strategies, endpoint thinking, adaptive planning, retrospective learning and operational feasibility. These uses are connected by one practical question. Can the data help the team make a better design decision before the trial becomes difficult to run?

For example, AI and machine learning in clinical trials may help teams explore whether eligibility criteria are too restrictive, whether a proposed endpoint is supported by earlier evidence, whether certain patient subgroups are more likely to benefit, or whether an adaptive feature is operationally realistic. These insights still need statistical, clinical and regulatory review before they influence a protocol.

In clinical trials, especially those with complex datasets, the ability to retrospectively analyse results is crucial for understanding how different subpopulations respond to treatment. Advanced retrospective analysis involves using sophisticated statistical methods and AI-driven tools to detect heterogeneous treatment effects, identify subpopulations that respond differently, and evaluate endpoints likely to succeed in future trials.

Companies like PhaseV specialise in applying these advanced analytical techniques to past trial data. By leveraging AI and machine learning, these analyses can identify specific subpopulations within a trial cohort who may respond more favourably to the treatment.

Adaptive trial designs allow modifications to trial parameters based on interim data. This can include altering dosage, changing patient groups, or modifying endpoints. In practice, those adaptations need to be planned, governed and reviewed carefully so that flexibility does not undermine the reliability of the trial.

One of the most important applications of AI in clinical research is protocol feasibility.

Clinical protocols define how a study will be conducted, including eligibility criteria, treatment schedules, monitoring requirements, and outcome measurements. While strict criteria are necessary for scientific validity, overly complex protocols can create barriers to recruitment and increase operational difficulties.

AI-powered feasibility tools can analyse historical clinical trial data, healthcare databases, and operational information to evaluate whether a proposed study design is realistic. AI models can help assess:

• potential patient availability

• recruitment timelines
• eligibility criteria complexity
• site performance
• operational risks
• previous trial outcomes
• patient burden, including visit schedules and assessment load
• amendment risk, including criteria that may be difficult to implement 
• scenario review, such as how design changes affect enrolment assumptions

AI analysis may identify that a specific inclusion criterion could significantly reduce the available patient population. Researchers can then evaluate whether the requirement is scientifically essential or whether modifications could improve recruitment without affecting trial objectives. 

AI-supported feasibility assessment allows research teams to identify challenges earlier and make better-informed design decisions. NLP can also support this work by extracting information from protocols, eligibility criteria, clinical notes, pathology reports and relevant literature. This can help teams see where a proposed design may be difficult to apply in routine clinical settings.

For example, Novartis utilised NLP in its "data42" project to digitise and analyse over 20 years of clinical trial protocols and patient data [2]. This enabled the identification of previously unknown correlations between drugs and diseases, facilitating the generation of new hypotheses for clinical trials. By converting unstructured historical data into structured formats, NLP helps researchers explore connections that would be difficult to discover manually, ultimately informing more targeted and efficient clinical trials.

Patient recruitment remains one of the biggest challenges in clinical trials.

A study may have a scientifically strong design, but delays in identifying eligible participants can significantly increase development timelines and costs. AI can support recruitment by analysing information from multiple sources, including:

• electronic health records (EHRs)

• genomic data
• laboratory results
• medical imaging
• pathology reports
• previous treatment information

AI-driven predictive modelling on large datasets, including EHRs, genetic data, and other biomarkers is now being used to identify patients who are most likely to benefit from a treatment. This is particularly valuable in precision medicine, where trials often focus on biomarker-defined populations.

For example, AI-assisted approaches may support identification of patients with molecular characteristics such as:

• EGFR mutations in lung cancer

• HER2-positive cancers
• KRAS G12C alterations in selected tumour types

The FDA utilised machine learning techniques to identify a suitable patient population for an emergency use authorisation of Anakinra for COVID-19 Treatment. During the SAVEMORE trial, AI was used to predict which COVID-19 patients would have high suPAR levels, a biomarker associated with severe disease outcomes. By combining predictive modelling with clinical trial data, the FDA was able to identify patients who would most likely benefit from Anakinra treatment under an Emergency Use Authorisation (EUA) [1].

AI does not make final decisions about patient participation. Eligibility must continue to follow approved protocols, clinical evaluation, informed consent requirements, and regulatory standards. AI-supported patient matching should also be reviewed for representativeness, so that recruitment assumptions do not exclude relevant patient groups or rely too heavily on incomplete data. Clinician verification remains essential, particularly when AI uses unstructured EHR data, pathology text, or historical treatment information.

Recruitment is also a workflow issue. AI may help identify potential participants, but sites still need suitable outreach processes, privacy controls, patient-facing materials and retention planning. A design that finds eligible patients but creates excessive visit burden or poor follow-up will still be difficult to deliver.

AI for clinical study design is often discussed before a trial starts, but some uses continue into conduct. These should be treated carefully, because they can affect data quality, patient oversight, and operational decision-making.

AI can support source abstraction, query triage, digital health technology data review, adherence monitoring, ePRO and eCOA signal review, visit-window forecasting and safety signal detection. It can also help teams identify patterns linked to recruitment delays, patient dropout, protocol deviations and site workload.

These uses should not remove accountability from the study team. If AI affects monitoring, data review or safety-related classification, the process needs clear documentation, appropriate validation, auditability and human review.

Although AI provides analytical capabilities, human expertise remains essential in clinical research. Successful clinical trials require collaboration between:

• clinical researchers

• physicians
• biostatisticians
• regulatory experts
• data scientists
• patient representatives

AI can identify patterns and generate predictions, but experts must determine whether those insights are scientifically meaningful and clinically appropriate.

For example, an AI system may identify a relationship between a biomarker and treatment response. However, researchers must evaluate whether the finding has biological relevance and whether it should influence trial design.

However, despite its potential, AI adoption in clinical trial design requires careful management. Important considerations include:

Data quality and bias
AI systems depend on high-quality data. Incomplete datasets, inconsistent information, or underrepresentation of certain patient groups can affect AI performance. Interoperability also matters, because data from EHRs, claims, registries, imaging systems and digital tools may not be collected or coded in the same way.

Transparency and explainability
Researchers and regulators need confidence that AI-generated insights are understandable and scientifically reliable.

AI should be judged by whether it improves a real study decision or operational outcome, not by model accuracy alone. Useful measures may include feasibility cycle time, screen-failure rate, amendment avoidance, recruitment performance, retention, data quality, representativeness and reduced site burden.

This does not mean every trial needs AI. In some studies, conventional statistical review, feasibility assessment and operational planning may be sufficient. AI is most useful when the design question depends on complex, distributed or unstructured data that would be difficult to review manually at the same scale.

AI and machine learning are supporting clinical trial design by helping researchers improve feasibility assessment, patient recruitment, and operational planning. The responsible use of AI in clinical trial design will be shaped by defined use cases, fit-for-purpose data, proportionate governance, and clear human accountability.

Traditional clinical trial analytics usually rely on established statistical methods applied to structured datasets. AI and machine learning can analyse larger and more varied information sources, including clinical notes, imaging data, genomic information, real-world healthcare records, and previous trial data.

This does not make AI a substitute for statistical analysis. In clinical research, AI is most useful when it helps identify patterns, risks or feasibility issues that can then be reviewed by clinical, statistical, and regulatory experts.

Yes. AI can be used in clinical trials to support specific tasks such as protocol feasibility assessment, patient matching, site selection, recruitment planning, data review and safety signal detection.

AI should be treated as a decision-support tool, not a replacement for clinical, statistical or regulatory judgement.

In clinical trial design, AI can help research teams assess whether a proposed study is feasible, identify suitable patient populations, review eligibility criteria, explore endpoint assumptions and evaluate operational risks before a trial begins.

In scientific writing, AI can assist with tasks such as literature screening, drafting support, consistency checks, terminology review and summarising structured information. However, scientific writing in clinical research still requires expert review to ensure accuracy, traceability, regulatory suitability and appropriate interpretation of the underlying evidence.

At Quanticate, we are at the forefront of integrating advanced AI and ML techniques into clinical trial design. Our team of experienced statistical consultants offer cutting-edge solutions to improve patient selection, enhance hypothesis generation, and implement adaptive designs, all while ensuring compliance with regulatory standards. For more information on how we can enhance your clinical trial with AI and ML, please request a consultation below.

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References:

1. Using Machine Learning to Identify a Suitable Patient Population for Anakinra for the Treatment of COVID-19 Under the Emergency Use Authorization | FDA
2. NLP Analyzes the Past to Inform the Future of Clinical Trial Design (appliedclinicaltrialsonline.com)