Clinical trial site selection remains one of the most consequential decisions in rare disease drug development, influencing enrollment timelines, data quality, patient diversity, and, ultimately, the success or failure of a study. Yet traditional approaches to feasibility analysis and site identification often rely on fragmented data sources, time-intensive manual review, and historical site relationships without current evidence of their enrollment potential.

The emergence of artificial intelligence (AI) platforms designed specifically for clinical trial planning presents an opportunity to enhance how sponsors and contract research organizations (CROs) approach pre-award landscape analysis. These tools promise to integrate vast datasets, simulate trial scenarios, and identify optimal site configurations with unprecedented speed and precision.

According to market analyst projections, the AI-powered clinical trial site feasibility sector will grow from $1.5 billion in 2025 to $3.6 billion by 2029, reflecting high confidence in these capabilities.
However, as with any transformative technology, the path from promise to practice requires careful navigation. Understanding where AI adds genuine value, recognizing its limitations, establishing appropriate evaluation criteria, and maintaining meaningful human oversight are essential to realizing the benefits while mitigating risks.

In this article, we examine the current landscape of AI-enabled site selection tools, the opportunities and challenges they present, and the frameworks needed to ensure their responsible use.

The Opportunity: What AI Can Deliver

Digital Site and Study Twin Platforms

Among the most promising developments in AI-enabled trial planning are digital twin platforms that create virtual representations of clinical research sites and study populations. These systems aggregate historical trial performance data, site operational characteristics, patient population profiles, and infrastructure capabilities into comprehensive digital models that can be queried and simulated.

Rather than relying only on site questionnaire responses and investigator relationships, sponsors and CROs can now evaluate sites based on their therapeutic area expertise, demonstrated enrollment velocity, protocol adherence patterns, and data quality metrics drawn from public and proprietary databases. Some platforms maintain continuously updated digital twins of thousands of research sites globally, enabling rapid identification of locations that match specific protocol requirements.

Trial simulation capabilities take this a step further by modeling how a protocol would perform across different country and site configurations. These platforms allow sponsors to pressure-test inclusion and exclusion criteria, visit and assessment schedules, and enrollment projections before committing resources, enabling more informed decisions about study footprint and helping to identify potential operational bottlenecks.

Real-World Data Integration for Finding Patients

Another category of emerging AI tools focuses on leveraging real-world data sources to identify where target patient populations actually receive care. These platforms integrate electronic health record (EHR) data, claims databases, laboratory results, and healthcare provider networks to map disease prevalence and treatment patterns at granular geographic and institutional levels.

Traditional feasibility relies heavily on investigator estimates of patient availability, which tend to be overly optimistic. By analyzing actual patient records that match trial eligibility criteria, these AI tools provide evidence-based enrollment projections that can significantly reduce the risk of under-enrollment. A recent analysis found that AI-driven site selection improved identification of top-enrolling sites by 30-50% and accelerated enrollment by 10-15% across different therapeutic areas.[i]

The ability to query unstructured clinical data is particularly valuable for finding patients. Often, eligibility criteria involve clinical details, such as specific tumor characteristics, symptom severity, and prior treatment responses, which are not captured in standard diagnostic codes but may appear in physician notes. Natural language processing (NLP) can extract these details from free-text documentation, expanding the scope and precision of patient identification beyond what can be provided by structured data alone.

Automation of Landscape Analysis

A third opportunity for AI involves reducing the manual burden of data collection, synthesis, and analysis of traditional feasibility assessment. Comprehensive landscape analysis requires gathering and analyzing information from diverse sources, including competitive intelligence databases, regulatory filings, published trial results, conference presentation, and site performance records, which can be extremely time-consuming.

AI platforms can automate much of this work, continuously monitoring public databases for relevant trial registrations, extracting protocol parameters from regulatory submissions, and synthesizing competitive landscape information into actionable intelligence. By reducing the time needed for data aggregation, these tools enable faster decision-making and allow human experts to focus on interpretation and study strategy.

[i] McKinsey & Company. Unlocking peak operational performance in clinical development with artificial intelligence, January 9, 2025. Available at https://www.mckinsey.com/industries/life-sciences/our-insights/unlocking-peak-operational-performance-in-clinical-development-with-artificial-intelligence,

Challenges and Limitations of AI in Clinical Trial Planning

Data Quality and Availability

The effectiveness of any AI system is intrinsically constrained by the quality and completeness of its underlying data. For example, EHR systems vary in their structure and comprehensiveness across institutions and geographies. Missing data is common, and important clinical variables may be absent or inconsistently documented. Claims data provides broad coverage but lacks clinical depth. Patient registries may offer clinical detail, but the duration of follow-up may be limited.

Another significant limitation may be geographic coverage. While AI platforms may have robust data in North America and Western Europe, coverage in emerging markets may be thin. Thus, it is critical to understand the underlying data sources of any AI tool.

Bias and Representativeness

Real-world data sources reflect the patient populations captured within them, which introduces selection biases that can propagate through AI analyses. Patients without regular healthcare access, those in underserved communities, and those facing barriers to care are systematically underrepresented. If AI tools optimized site selection based on these datasets, they may inadvertently perpetuate or exacerbate the lack of diversity that already characterizes clinical trial enrollment.

Sites identified by algorithms that have been trained on historical data may cluster in geographic areas and healthcare systems that have previously enrolled predominantly homogeneous patient populations. Consequently, thoughtful implementation of AI requires explicit incorporation of diversity considerations into site selection criteria rather than relying solely on optimization of enrollment speed.

Confidentiality

Using AI platforms for site selection and protocol optimization requires sharing detailed study information with technology vendors. Protocols contain sensitive competitive intelligence about development strategy, target populations, and study design. Therefore, sponsors and CROs must carefully evaluate the security of AI platforms and the terms that govern how data is shared, stored, and potentially used for model training. The bottom line is—any use of AI requires weighing efficiency gains against the risk of exposing proprietary information.

Essential Criteria for Evaluating AI Tools for Site Selection

Given the proliferation of AI platforms for clinical trial planning, sponsors and CROs need a structured framework for evaluating tools to incorporate into their workflows. At Ergomed, we use the following criteria to guide vendor assessment and evaluation.

Data Provenance and Coverage

  • What specific data sources does the platform integrate?
  • What is the recency of each data feed?
  • Which geographic regions and therapeutic areas have robust data coverage?
  • How does the platform handle data from countries with different data availability and privacy frameworks?
  • What validation has been performed to assess data accuracy and completeness?

Transparency and Validation of Algorithms

  • How does the AI model generate its recommendations?
  • Has the algorithm been validated prospectively against actual trial outcomes?
  • What performance metrics are available, including sensitivity, specificity, and predication accuracy, for enrollment rates?
  • Has the model been tested across different therapeutic areas and study designs?

Bias Assessment and Mitigation

  • What analysis has been conducted to identify potential biases in the model’s recommendation?
  • Does the platform include the capability to assess patient demographics and ensure diverse site recommendations?
  • Are there mechanisms to weight diversity considerations into site optimization?

Integration and Usability

  • How does the platform integrate with existing clinical trial management systems and workflows?
  • What training is required?
  • Can outputs be customized to meet specific organizational requirements and reporting needs?

Security and Compliance

  • What data security certifications does the vendor maintain?
  • How is proprietary information protected from unauthorized access or use in model training?
  • Does the platform comply with relevant regulatory frameworks including HIPAA and GDPR?

Ongoing Need for Human Oversight

Despite the capabilities of AI-enabled tools, human expertise remains essential throughout the site selection process. CROs that maintain site relationships and networks have insights that are not captured in AI platforms, such as which investigators are nearing retirement, which sites are experiencing staff turnover, or which institutions might be involved in competing trials. In addition, site selection may be influenced by strategic considerations beyond enrollment optimization. For example, a sponsor may prioritize academic centers that enhance scientific credibility or choose sites that support specific regulatory filing strategies.

At Ergomed, we view AI recommendations as hypotheses requiring verification rather than decisions. By keeping the human in the loop, we verify and validate all AI outputs, applying our judgment and decades of clinical trial experience to ensure informed implementation of any site selection strategy.

Key Takeaway

The ability to integrate diverse data sources, simulate study scenarios, and identify optimal site configurations using AI platforms offers potential to accelerate enrollment and reduce clinical trial failures. However, effective deployment of these tools requires a deep understanding of both the capabilities and current limitations of AI. The most successful implementations will be those that treat AI as an augmentation of human expertise, rather than a replacement for it.

The technology landscape is evolving rapidly, and regulatory frameworks are being put in place to address AI in the context of clinical development. Organizations that develop institutional competency in AI-enabled clinical trial planning and site selection now will be better positioned to leverage future advances, with the goal of conducting studies that enroll more quickly, produce higher-quality data, include more diverse populations, and bring effective therapies to patients more efficiently.