Clinical trials serve as the gold standard in medical research, but researchers are often faced with challenges in identifying patients for these trials and in the planning stages. These trials are inherently labor-intensive, complex, and highly regulated, necessitating the commitment of significant resources and the involvement of highly specialized staff with extensive expertise in the research. Insufficient funding, among other factors, is a major barrier to conducting clinical trials, primarily due to the labor- and cost-intensive nature of these trials.
Generative AI, especially large language models (LLMs), has shown considerable promise in streamlining the entire process, indicating a transformative shift in clinical trials. This article delves deep into the multifaceted applications of LLM in the field of clinical trials, matching patients to trials, streamlining trial planning, analyzing free-text narratives for coding, etc.
LLM in Clinical Trial
Generative AI is already transforming clinical trials in several ways and accelerating the pace of overall drug discovery. Let’s have a look at the role large language models might play in several aspects of a clinical trial:
Matching Patients to Trials
Identifying patients eligible to participate in a clinical trial is an intricate and labor-intensive process. This process involves analyzing a patient’s medical history, matching their profile to eligibility criteria, and then determining suitable clinical trials based on the assessment. The traditional matching process is labor-intensive, time-consuming, prone to human errors, and often fails to reach completion.
The incorporation of large language models (LLMs) in patient-trial matching alleviates these challenges. As per research, an LLM tested on 146 clinical trials, each with a set of eligibility criteria (a total of 4,135 eligibility criteria), achieved an accuracy of 72% (2,994/4,135) in correctly identifying patient screenability. LLMs can partially automate the pre-screening process, streamlining the evaluation of eligibility criteria and reducing the time and expertise needed.
Large language models can evaluate and assign scores to patients based on multiple factors, facilitating a nuanced understanding of their suitability for clinical trials and boosting the precision of the matching process. Moreover, their generative AI capability enables these models to explain their reasoning process when producing the output. This helps physicians review and understand the decision-making process in detail.
Streamlining Clinical Trial Planning
Clinical trial planning is a complex, time-consuming, and risky aspect of drug discovery. The traditional process involves the manual review and analysis of vast amounts of data from a variety of sources, including prior research, regulatory guidelines, and certain therapeutic goals. The transformation of this information into straightforward and compliant ‘trial protocols’ needs significant expertise and resources.
AI algorithms can address these challenges by efficiently processing extensive text data and condensing large volumes of clinical trial descriptions into straightforward and actionable information so that researchers can easily understand it. LLM-based models can be employed to analyze a large amount of clinical trial data, which will help investigators quickly understand key insights.
In a nutshell, large language models can potentially streamline clinical trial planning by procuring important information from complex clinical text data, saving researchers time and effort devoted to the analysis of vast amounts of material. Moreover, LLMs pre-trained on a diverse array of clinical trial documents can analyze patterns and potentially predict the outcome of a trial.
Large language models have demonstrated a multifaceted approach to clinical trial planning – from reviewing and analyzing complex information to producing clear text output and even offering predictive insights.
In Technical Writing
Language models can be utilized to automate various forms of paperwork that traditionally require human expertise. In the medical field, large language models are increasingly being utilized for routine technical writing tasks. This helps reduce the administrative burden associated with clinical trial documentation, ultimately freeing up substantial time and resources for healthcare staff.
LLMs can be used to synthesize clinical information into a concise and easy-to-understand format for patients. The methodology known as “chain-of-thought prompting” can help draft documents requiring high-level reasoning. The method guides the language model to generate intermediate steps of reasoning, ensuring straightforward and contextually relevant output.
LLM-based applications can not only automate clinical text documents but also proficiently analyze and convert tabular data, expanding the potential applications in streamlining clinical trial documentation and accelerating research.
In Obtaining Informed Consent
It is essential to seek informed consent to protect the rights and safety of participants in clinical trials. Research suggests participants may not fully comprehend the information about the trial before enrolling. LLMs enable participants to ask and get their queries resolved in natural language. Studies indicate that LLMs are capable of providing accurate and appropriate answers with empathy to specific medical queries.
LLM-enabled chatbots fed with updated information about the clinical trial allow participants to ask questions and get instant, informed responses. This approach could redefine informed consent in clinical trials by enabling participants to access information in a more interactive and user-friendly way, fostering deeper understanding.
Conclusion
Large language models are fostering transformative changes in drug discovery, and clinical trials can benefit considerably from innovations in generative AI. These developments can significantly increase efficiency and accelerate clinical trials. LLMs can efficiently streamline and execute traditionally repetitive and labor-intensive tasks across the entire clinical trial process, from patient-to-trial matching and clinical trial planning to technical writing and fostering more cognizant consent.
However, the incorporation of LLMs into clinical trials has its share of challenges, particularly in relation to legal and quality aspects. LLMs are prone to ‘hallucination’, meaning they can generate misleading or incorrect information. Moreover, it is difficult to understand the functionality of AI models, which further complicates their application in real-world scenarios with critical decision-making.
The integration of LLMs into clinical trials requires careful financial investment and examination to leverage its transformative benefits.
