Modern medicine is a miracle, and treatments that were previously unimaginable have been widely used. Think of advanced medical devices, such as implantable defibrillators that help regulate heart rhythm and reduce the risk of cardiac arrest.
Without clinical trials, such breakthroughs are impossible, and it is a rigorous study to evaluate the impact of medical interventions on human participants.
Unfortunately, over time, the clinical trial process becomes slower and more expensive. In fact, one in seven drugs entering the Phase I trial were finally approved – the first phase of testing safety. Currently, it requires an average of nearly $1 billion in funding and a decade of work to bring a new drug to market.
Half of this time and money are spent on clinical trials that face increasing barriers, including inefficient recruitment, limited diversity and inaccessibility to patients. Therefore, the speed of drug discovery will slow down and the cost will continue to rise. Fortunately, the latest advancements in artificial intelligence have the potential to disrupt trends and improve drug development.
From models that predict significant accuracy in complex protein interactions to AI-driven lab assistants simplifying routine tasks, AI-driven innovations have reshape the drug landscape. Adopting new AI capabilities to address clinical trial barriers can enhance the trial process of patients, doctors and biopharmaceuticals, pave the way for new and influential drugs, and lead to better health for patients.
Obstacles to drug development
Throughout the clinical trials, developing drugs face many challenges, resulting in shocking approval rates from regulators such as the U.S. Food and Drug Administration (FDA). As a result, many research drugs have never entered the market. Key challenges include trial design setbacks, few patient recruitment and limited access and diversity of patients – problems that exacerbate each other and hinder the progress and equity of drug development.
1. Test site selection challenge
The success of a clinical trial depends heavily on whether the trial location (usually a hospital or a research center) can recruit and enroll sufficient qualified research populations. Field selection is traditionally based on several overlapping factors, including historical performance in previous trials, local patient populations and demographics, research capabilities and infrastructure, available researchers, recruitment deadlines, and more.
Each standard itself is very simple, but the process of collecting each standard is full of challenges and the results may not reliably indicate whether the site is suitable for trials. In some cases, the data may be only outdated or incomplete, especially if validated in only a small number of studies.
Data that helps determine the selection of a website also comes from different sources, such as internal databases, subscription services, suppliers or contract research organizations that provide clinical trial management services. Due to so many convergence factors, it can be confusing and complex to summarize and evaluate this information, which in some cases can lead to suboptimal decisions at the trial site. As a result, the organization conducting clinical trials—may or underestimate its ability to recruit patients in the trial, resulting in waste of resources, delays and lower retention rates.
So, how does AI help plan the selection of experimental locations?
By training AI models using historical and real-time data from potential sites, trial sponsors can predict patient enrollment and site performance – optimizing field allocation, reducing over- or under-registration, and increasing overall efficiency and costs. These models can also rank potential sites by identifying the best combination of site attributes and factors that align with research objectives and recruitment strategies.
The AI model is trained through clinical trial metadata, medical and pharmacy claims data, and patient data from membership (primary care) services can also help identify clinical trial locations that will provide access to a variety of relevant patient populations. These sites can be centrally located in underrepresented groups, or even in popular locations within the community, such as barber shops or faith-based community centers and community centers, help address barriers to patient access and lack of diversity.
2. Recruiting low patients
Patient recruitment remains one of the biggest bottlenecks in clinical trials, consuming up to one-third of the duration of the study. In fact, one in five trials failed to recruit the required participants. As trials become more complex – with additional patient touch points, stricter inclusion and exclusion criteria, and increasingly complex study design – the recruitment challenges continue to grow. Not surprisingly, the study linked the rise in protocol complexity to the decline in patient enrollment and retention.
Most importantly, strict and often complex eligibility criteria designed to ensure participants’ safety and research integrity often limit treatment and disproportionately exclude certain patient populations, including older people and ethnic, racial, and gender minorities. In oncology trials alone, an estimated 17-21% of patients are not allowed to enroll due to restricted eligibility requirements.
AI is prepared to optimize patient qualification standards and recruitment. Although recruiting traditionally requires doctors to screen patients manually (which is very time-consuming), AI can effectively and effectively match the patient profile to the appropriate trial.
For example, machine learning algorithms can automatically identify meaningful patterns in large datasets, such as electronic health records and medical literature, to improve patient recruitment efficiency. The researchers even developed a tool to quickly review candidates using large language models and help predict patient eligibility, reducing patient screening time by more than 40%.
HealthTech, which employs AI, is also developing tools that help doctors quickly and accurately determine patients’ qualified trials. This supports the acceleration of recruitment, potentially allowing trials to begin faster, thus providing patients with an opportunity to obtain new research treatment earlier.
3. Limited patient accessibility and diversity
AI can play a key role in improving the scope of access in clinical trials, especially for patients in underrepresented populations. This is important because difficult-to-get performance and limited diversity not only contribute to low patient recruitment and retention, but also lead to imbalance in drug development.
Consider that clinical trial sites often gather in urban areas and large academic centers. As a result, these trials are often not accessible to communities in rural or underserved areas. Financial burdens such as treatment costs, transportation, parenting, and the lack of work exacerbate barriers to trial participation and are more evident in ethnic and racial minorities and groups with lower socioeconomic status than average.
As a result, although 39% of the national population, racial and minority groups account for 2% of patients in clinical trials in the United States. This lack of diversity poses a significant risk in genetics, which vary among racial and ethnic populations and may affect adverse drug responses. For example, Asians, Latinos and African Americans with atrial fibrillation (an abnormal heart rhythm associated with heart-related complications), who take warfarin (a drug that blocks blood clots) have a higher risk of brain edema than European descent.
Therefore, greater representation in clinical trials is crucial to helping researchers develop treatments that are both effective and safe, ensuring that medical advances benefit everyone, not just some demographic groups.
AI can help clinical trial sponsors solve these challenges by facilitating dispersed trial activities (transferring trial activity to remote and alternative locations, rather than collecting data at traditional clinical trial sites.
Decentralized trials typically use wearable devices that collect data digitally and use AI-driven analytics to summarize relevant anonymous information about trial participants. Combined with electronic check-in clinical trial composition methods, geographic barriers and transportation burdens can be eliminated, allowing trials to be used by a wider range of patients.
Smarter trials make smarter therapy
Clinical trials are another department that translates from AI. With the ability to analyze large data sets, identify patterns and automate processes, AI can provide holistic and robust solutions to today’s barriers – optimize trial design, enhance patient diversity, simplify recruitment and retention, and break accessibility barriers.
If the healthcare industry continues to adopt AI-driven solutions, the future of clinical trials has the potential to become more inclusive, patient-centered and innovative. Adopting these technologies is not just about keeping up with modern trends, but also involves creating a clinical research ecosystem that can accelerate drug development and provide more equitable healthcare outcomes for all.
