In the ever-evolving world of clinical trials, automation has long been a dream—one that could streamline operations, reduce costs, and accelerate timelines. Yet, for years, traditional machine learning (ML) models promised much but delivered far too little. They demanded vast amounts of structured data, painstaking feature engineering, and were limited in scope. But now, with the rise of large language models (LLMs), that dream is becoming a reality.

The Challenges of Traditional ML in Clinical Trials

The promise of traditional ML lies in its ability to learn from data, but therein lies the challenge: data availability. Clinical trials are notorious for generating complex, variable data, often spread across different sites, protocols, and patient populations. Traditional ML requires large, structured datasets—something that simply isn’t available in most clinical trials.

Take, for example, patient recruitment—a critical bottleneck in clinical trial conduct. Traditional ML models might require historical, structured patient data to predict eligibility for a new trial. But in reality, patient data is often scattered across various formats, from electronic health records (EHRs) to handwritten notes. Even when structured data is available, the variability in trial protocols makes it nearly impossible to create reusable models. Each trial demands a new model, designed from scratch, leading to limited success in automating these processes. The effort, time, and expertise required often outweighed the benefit, making traditional ML a nonstarter in most automation use cases.

Enter large language models (LLMs)

Unlike traditional ML, which struggles with structured data and narrow use cases, LLMs can process vast amounts of unstructured text. It bypass the need for extensive feature engineering. This flexibility means that LLMs don’t need the carefully curated datasets that traditional ML requires. Instead, they can work with whatever data is available—protocol documents, regulatory filings, clinical notes—and still deliver results.

Consider the task of trial protocol generation. In the past, automating the creation and review of trial protocols was almost impossible. But LLMs can read through existing protocols, guidelines, and regulatory documents to create drafts, suggest improvements, and ensure compliance. Companies are now exploring use of LLMs to accelerate protocol design, saving months in the trial initiation process.

Another powerful example is adverse event reporting. Traditional ML models require structured adverse event datasets and are limited in scope. But LLMs can review free-text safety reports, identify patterns, and even summarize findings across hundreds of documents, significantly improving the efficiency of safety monitoring in clinical trials.

And with Retrieval-Augmented Generation (RAG), LLMs can take this even further. By combining real-time data retrieval with text generation, LLMs can deliver highly contextual answers. Imagine a scenario where an LLM can access a database of past clinical trials and instantly pull relevant information into its responses. It can generate highly specific, context-aware content that traditional ML simply can’t.

Real-World Application of LLMs in Clinical Trials

Let’s look at an example from the world of patient matching. Traditionally, ML models would need access to clean, labeled datasets to match patients to trials. But with LLMs, you don’t need pristine datasets. By feeding them patient records, even if unstructured, and clinical trial protocols, LLMs can analyze both and suggest patient matches almost instantaneously. This reduces the burden on clinical trial coordinators, enabling faster recruitment and more efficient trial management. Imagine an LLM sifting through patient eligibility criteria in real-time, flagging suitable candidates, and drastically reducing the time to fill trials.

Conclusion

In the absence of large, structured datasets, traditional ML was never considered a feasible solution for automating clinical trial conduct. The challenges of designing, engineering, and processing data for each individual use case led to limited success, even in highly targeted applications. But LLMs have changed the game. They bypass the data constraints that held ML back and offer rapid, scalable solutions that are transforming clinical trial automation today.

The dream of clinical trial automation is within our reach, and it’s powered by LLMs.


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