Picture by FGK with help of AI
Drug development has always demanded precision, patience, and enormous resources. Now, artificial intelligence (AI) systems are changing what’s possible − by giving research teams sharper tools to succeed in the preclinical drug development phase and clinical trials.
What is AI in clinical trials?
AI in clinical trials brings automated, data-driven methodologies to processes that once relied entirely on manual effort, touching every stage from early phase through post-market surveillance. AI in clinical development can be beneficial by
- accelerating and refining recruitment and stratification,
- improving study protocol design and enabling adaptive conduct,
- providing continuous, high‑resolution safety and data‑quality surveillance,
- automating complex endpoint assessment,
- making decentralized, patient‑centric models feasible at scale,
- improving regulatory compliance efforts
- increasing operational efficiency, and thus lowering costs,
- and supporting more personalized studies.
However, these benefits of AI in clinical research depend heavily on data quality, rigorous validation, and robust oversight to avoid bias and ensure regulatory compliance and ethical acceptance.
The Spectrum of AI Applications in Clinical Research
AI‑enabled tools can automate labor‑intensive tasks, improving data integrity and freeing human resources for scientific decision‑making. Across oncology and other fields, narrative reviews argue that AI can shorten timelines, reduce avoidable protocol amendments, and improve site and country selection by learning from historical performance and epidemiology [1]. AI in clinical research is useful by automating and improving various aspects of trials:
1. Protocol Design and Feasibility Analysis
Protocol development sets the trajectory for everything that follows. A poorly designed study protocol has eligibility criteria that exclude too many patients, endpoints that fail regulatory scrutiny, and timelines that collapse under their own assumptions.
AI in clinical trials can address this at the source:
- Large language models (LLM) can extract structured data from thousands of historical trials to generate robust eligibility criteria, using in-context learning and explicit reasoning chains that make outputs interpretable.
- Predictive systems go one step further by integrating multimodal data (clinical data, imaging data, omics data, real-world data), as AI can learn predictors of treatment response and toxicity, which in turn inform the design of biomarker-guided studies as well as platform or umbrella studies. This increases the chances that early‑phase trials identify the right patients and relevant signals, improving the translational yield per participant.
- Machine‑learning‑based simulations and predictive models can inform endpoint selection, sample‑size assumptions, inclusion/exclusion criteria, and randomization schemes, making protocols more realistic and efficient. During conduct, AI‑driven adaptive designs can use interim data to adjust randomization ratios, dosing, or inclusion criteria, or to stop early for futility, improving both efficiency and ethical balance [2].
The result: protocol decisions have more data foundation beneath them which can reduce the number of later protocol amendments, improve trial design and ultimately save time and costs.
2. Patient Recruitment and Retention
Patient Recruitment is where clinical trials most visibly struggle and finding the right study sites is a task sponsors should place at the top of their priority list.
AI-powered patient-trial matching algorithms process both structured data from electronic health records and unstructured data, which can boost recruitment in drug development studies and investigations with medical devices significantly. AI in clinical trials can mine electronic health records, registries, imaging, and sometimes omics data to identify eligible patients more quickly and accurately than manual screening, reducing screen failure rates and shortening patient recruitment timelines. To accurately identify potentially eligible patients at one study site could now take minutes instead of days.
Retention poses a separate challenge. Predictive models that draw on genetic profiles and medical histories identify dropout risk in forecasting adverse drug reactions (ADR) in older patients, for example. Conversational AI and chatbots can improve participant engagement through real-time support, tailored guidance, and personalized reminders − keeping patients connected to the study throughout its duration.
Reviews of AI in clinical research highlight advantages for decentralized clinical trials in the fields of remote recruitment, e‑consent optimization, continuous safety monitoring, and automated capture of patient‑reported and physiological outcomes, which together have the ability to reduce burden on participants and sites [3].
3. Data Management and Real-World Data Analytics
Natural language processing (NLP) can automate extraction from unstructured clinical sources, streamlining case report form (CRF) completion and cutting administrative burden at scale. In decentralized and hybrid trials, AI helps interpreting continuous data from remote monitoring, smartphones, and home devices, turning noisy real‑world signals into usable clinical measures.
4. Safety Monitoring and Risk-Based Pharmacovigilance
Safety monitoring has traditionally been retrospective: a review of what already happened rather than anticipation of what’s coming. AI in clinical research shifts that dynamic. Traditional methods often depend on manual data analysis at fixed intervals. AI processes information continuously across multiple data sources, with the ability of identifying safety signals in real time, predicting adverse events (AEs), and detecting symptom clusters that periodic reviews might miss entirely.
AI systems bring the possibility of scanning accumulating trial data (vitals, labs, device streams, free‑text notes) to detect emerging safety signals, protocol deviations, and data anomalies earlier than traditional periodic review. This supports faster risk mitigation, more focused on‑site or centralized monitoring, and can lower the probability of undetected systematic errors.
5. Enhanced Endpoint Detection and Richer Phenotyping
For complex endpoints (e.g. imaging, endoscopy, pathology, wearables, digital biomarkers), AI in clinical research, especially deep learning and NLP, can standardize, quantify, and automate assessments, reducing inter‑observer variability and enabling more sensitive or composite endpoints. In inflammatory bowel diseases (IBD), for example, AI in clinical trials has been explored for automated endoscopic scoring and multidimensional disease activity indices that can serve as refined trial endpoints [2].
However, it is important not to forget to always involve people in the process to ensure that any erroneous results produced by AI can be identified. The use of AI should never require blind trust, as AI cannot build and convey the same level of trust as humans.
Does AI accelerate Drug Development Timelines?
The honest qualifier: clinical trial duration, regulatory review, and manufacturing scale-up remain largely unchanged. Visit schedules and regulatory requirements still set their own pace. AI in clinical trials cannot override them completely, though they can add beneficial speed by influencing processes as outlined above. But overall development times are reduced less dramatically than figures from preclinical development might suggest, a distinction that should be understood before setting expectations. Nevertheless, the fact that AI has proven capabilities to enhance efficiency, reduce costs, and improve patient outcomes justifies the view that AI is radically transforming both preclinical and clinical development.
Comparison of Traditional vs AI-Enabled Clinical Trial Approaches
The table below illustrates where differences between traditional and AI-enabled clinical trial methods matter most, including an assessment of how far AI methods have already progressed:
Traditional vs. AI-Enabled Methods in Clinical Research
| Aspect | Traditional methods in clinical research | AI‑enabled methods in clinical research |
|---|---|---|
| Protocol design | Expert‑driven, manual synthesis of prior trials, guidelines, and feasibility data; limited ability to simulate many alternatives. | Model‑assisted design using historical trial and real‑world data to predict recruitment feasibility, event rates, and site performance; (supports scenario testing but still requires human oversight and regulatory justification) [4] |
| Phase 1 success probability | Historically constrained by empirical chemistry/biology and conventional target selection; success rates vary by modality and indication. | For a small number of AI‑discovered candidates, early‑phase progression looks promising, but sample size is too small to claim a general increase; (current consensus is “potential uplift, not yet quantified robustly”) [1] |
| Patient eligibility screening | Manual chart review and clinician referrals; slow, error‑prone, and often leads to under‑enrollment or biased populations. | Natural‑language‑processing (NLP) and rules plus Machine Learning (ML) systems applied to electronic health records and registries to pre‑screen large populations, improve matching to complex criteria, and support diversity goals (still requires clinician verification) [4] |
| Adverse‑event (AE) detection | Periodic, largely manual review of structured data and narratives; signal detection often retrospective, with variable sensitivity. | Automated screening of structured and unstructured data (e.g., labs, vitals, notes) to flag anomalies and potential AEs earlier and more consistently; (demonstrated improvements in specific pharmacovigilance and monitoring settings, but no universal benchmark) [5] |
| Safety signal detection | Retrospective signal detection (e.g., periodic data safety monitoring boards (DSMB) reviews, aggregate analyses); higher latency to action. | Near–real‑time central monitoring that continuously evaluates accumulating data for emerging patterns, enabling earlier DSMB alerts and adaptive responses |
| Data collection | Discrete visits, site‑centric assessments, and paper or traditional eCRFs; limited temporal resolution. | Integration of continuous or high‑frequency data streams from wearables, apps, and home devices, with AI models denoising signals and deriving digital biomarkers, especially in decentralized or hybrid trials [2], [4] |
Table 1: 2026 – overview of Aspects in clinical trials in which the differences between conventional and AI-supported methods are most evident
Balancing Innovation and Compliance in AI-Driven Trials
Deploying AI in clinical research is not simply a technical decision. Regulatory scrutiny across multiple jurisdictions adds layers of complexity that sponsors must plan for deliberately. Each geographic region carries distinct timelines, validation standards, and documentation requirements and agencies are raising the bar on algorithmic transparency faster than many sponsor organizations anticipated. AI models also risk producing biased outcomes if training datasets lack diversity, potentially impacting underrepresented populations in clinical trials. [2]
The benefits are real. But so are the obstacles. Sponsors considering AI deployment should weigh both clearly.
AI in Clinical Trials: Data privacy as challenge
Data privacy sits near the top of this challenge list. AI systems frequently require data sharing across jurisdictions operating under different regulatory frameworks, e.g. HIPAA in the U.S., GDPR in Europe, each with distinct and tight consent requirements. The re-identification risk is not theoretical, a fact that also the EDPB Guidelines 02/2026 on Anonymization address explicitly [6]. Algorithms have, e.g., successfully re-identified 85.6% of adults in physical activity datasets, even after protected health information was removed. [7] Data protection will not be sidelined by AI; on the contrary, particularly when it comes to sensitive health data, even greater attention must be paid to it at present.
Regulatory Compliance
Regulatory frameworks are catching up on AI governance, the FDA released a draft guidance in January 2025 providing a establishing seven-step risk-based credibility assessment framework for AI model validation. [8]
The EU AI Act’s high-risk provisions take effect August 2, 2026, with the potential to classify certain drug development AI applications as high-risk. The EU AI Act also classifies AI systems used in clinical settings as high-risk when integrated into medical devices, for example. Ethics committees and national authorities apply increased scrutiny to such systems.
Governance and Transparency Requirements
Sponsors must demonstrate robust AI governance by model architectures, training data provenance, validation evidence, and other governance procedures BEFORE deployment. They must prepare specific technical documentation before any operative use begins, with ongoing updates required throughout deployment to ensure compliance tasks.
In the US, most AI-enabled medical devices currently clear through 510(k) pathways rather than more rigorous premarket approval processes, a gap that regulators are actively working to close.
EU AI Act Article 10 [9] mandates proper data governance for training, validation, and testing sets, requiring adequate management procedures throughout. Record-keeping obligations extend across the entire operational lifetime. Human oversight provisions ensure that people working with AI systems genuinely understand their capacities and limitations, not just in theory, but during day-to-day use.
Sponsors who treat validation as a checkpoint at the end of development will find themselves facing avoidable delays. Those who build credibility assessment into their workflows from the start, much like embedding quality management throughout a trial rather than concentrating it before authority inspection, are far better positioned for regulatory success.
The Future of AI in Clinical Trials: What Comes Next?
Clinical research now stands at a point where AI tools can generate synthetic patient data, simulate trial scenarios, and predict outcomes − before a single human participant enrolls. The implications for drug development are significant, provided the science keeps pace with the promise.
The most meaningful shift AI in clinical research brings to trial management is directional: Traditional oversight responds to problems after they surface, AI identifies, i.e. protocol deviations and safety signals BEFORE they escalate into serious adverse events, shifting the entire approach from reaction to anticipation.
This is where the value of AI in clinical research becomes most apparent. Not just faster processing or automated coding, but the ability to act on information while there is still time to change course.
Our Approach: How FGK Embraces AI and Innovation
At FGK, we take these developments seriously. Equitable access for everyone to AI-driven clinical trial innovations cannot be assumed. Small sponsor companies don’t have the resources large Pharma has for integrating AI into their whole development chain. Academic medical centers, community health systems, and independent research sites all need a seat at the “AI in clinical trials” table. FGK’s commitment to support small and mid-sized biotech and device companies, including academia, extends naturally into this space where responsible AI adoption, honest governance, and practical implementation go hand in hand.
FGK uses AI solutions when they benefit our clients and we can simultaneously ensure that the data remains secure, is not used for machine learning training purposes, and is subject to data protection regulations (e.g., the GDPR). There is no question that developments in AI are driving clinical research forward and will continue to do so even more than before. The strict regulations governing clinical trials also mean that some solutions already in use in other industries will take some time to significantly accelerate clinical research as well. This is, however, a small price we are willing to pay to continue protecting patient data and confidential information as effectively as possible in the face of innovative developments.
Conclusion
Proactive trial management − where AI identifies risks before they escalate − is within reach. All sponsors who engage with AI in clinical research face the same core challenge: capturing the efficiency without sacrificing the integrity. That balance is achievable, but it demands the same careful planning and expert guidance that successful clinical trials have always required.
The gains only hold when sponsors and CROs take governance seriously. Data quality and regulatory validation are not secondary concerns to manage after deployment − they are the foundation on which trustworthy AI-driven clinical trials are built.
The path forward is clear. Start with the right framework, invest in validation from day one, and build AI into your clinical program as a trusted tool, not a shortcut.
FAQs about ai in clinical trials:
How can AI improve clinical trials?
AI can improve several processes in clinical trials, e.g.: Data-driven development of inclusion and exclusion criteria and identification of the appropriate patient population; support in selecting endpoints, biometric assumptions, and analyses; improved patient-trial matching; strengthening patient retention through “personal” interaction and reducing burdensome procedures for patients; simplifying the consolidation and analysis of data from various sources; real-time detection of safety signal clusters; automated endpoint analysis.
Will AI replace humans in the conduct of clinical trials?
No, AI in clinical development for drug therapies and medical devices still requires qualified humans in the loop, at sponsor companies, clinical study sites and clinical research vendors, in order to uphold data privacy requirements, high data quality and ICH GCP compliance. AI governance needs to be implemented by humans in every development step when using AI in clinical research.
What are the main concerns of using AI in clinical trials?
The main concerns regarding the use of AI in clinical trials relate to data protection, data quality (specifically, AI hallucinations), transparency, and the governance requirements of the AI solutions used, as well as biases resulting from the training data employed.
Key references
- AI for clinical trials in oncology Available at: https://www.esmorwd.org/article/S2949-8201(25)00547-8/fulltext
- Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC11848901/
- Decentralized clinical trials and opportunities with artificial intelligence. Available at: https://www.researchgate.net/publication/387593489_Decentralized_Clinical_Trials_and_Opportunities_with_Artificial_Intelligence
- Artificial intelligence in clinical trials – future prospectives Available at: https://medwinpublishers.com/BEBA/artificial-intelligence-in-clinical-trials-future-prospectives.pdf.
- How is artificial intelligence used in clinical trials?. Industry‑oriented summary of AI use cases in recruitment and monitoring. Available at: https://datavid.com/blog/artificial-intelligence-in-clinical-trials
- Guidelines 02/2026 on Anonymisation
- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2719130
- https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological
- https://artificialintelligenceact.eu/article/10/