Clinical trials are notoriously expensive, costing on average about $41,000 per patient . In a trial with hundreds of patients, costs can run into billions of dollars. They take years, sometimes decades, and still have an abysmally high failure rate. With so much at stake, bringing the right mix of patients into the trial matters.
However, finding them can be a challenge, especially when considering niche drugs that impact smaller populations with patients spread across many geographies and diverse groups. Identifying the right sample that covers all these differences, while providing the right data for the trial to proceed smoothly, is the first consideration for pharma companies on whether to proceed with drug development.
When we’re talking about life-saving drugs, compromise cannot be an option. Luckily smarter patient subtyping with analytics and artificial intelligence can help.
Patient subtyping solves the problem of patient heterogeneity by using biomarkers, genetic data, and real-world clinical records to group patients based on how they’re likely to respond to treatment. Instead of a one-size-fits-all approach, trials become targeted, making success more likely and accelerating the drug development pipeline.
Biomarkers are the backbone of patient subtyping, acting as molecular fingerprints that define how a disease behaves in different individuals. They come from genetic sequencing, blood tests, imaging scans, and even wearable health devices.
But a single snapshot isn’t enough. Diseases evolve, and so do patients. That’s where longitudinal data—tracking patients over time—becomes essential.
Why Patient Subtyping Matters?
Real-world clinical records don’t follow a neat timeline. Some patients get regular check-ups, others disappear for months. Traditional statistical models struggle to make sense of this irregularity, leading to biased results and incomplete insights.
To fix this, researchers are turning to time-aware analytical models that account for irregular time intervals. These models help identify meaningful trends in disease progression and treatment response, even when patient follow-ups are inconsistent.
“AI-powered subtyping helps mitigate issues of expensive trial failures by identifying patient groups more likely to respond positively to a given treatment, also improving drug efficacy assessments.”
Computational Change with AI and Machine Learning
Old-school clustering methods—k-means and hierarchical clustering—have historically been used for patient subtyping. While effective in grouping patients based on predefined parameters, these traditional approaches often fall short in handling the complexity of real-world healthcare data. Today’s patient subtyping relies on AI and machine learning to do the heavy lifting, offering more dynamic and data-driven insights.
Recurrent neural networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have emerged as key players in analyzing patient data over time. RNNs and LSTM networks are sequential learners — a type of Machine Learning (ML) model, specifically within the Deep Learning subset of ML — designed to process and analyze data where order and context matter. Unlike traditional neural networks, which treat each input independently, RNNs retain memory of past inputs, making them ideal for tasks that involve time-series data, natural language processing, and speech recognition.
LSTMs are dynamic models that recognize patterns in sequential data, making them ideal for studying disease progression and treatment response. Researchers have even developed time-aware LSTMs that account for irregular time intervals, ensuring that older data isn’t over-weighted while still capturing recent trends accurately. It enables the creation of more precise patient subtypes, which is crucial for optimizing clinical trials.
Beyond LSTMs, the integration of multi-modal data—combining genomics, imaging, real-world clinical records, and even wearable health data—has opened new frontiers in personalized medicine. Advanced AI techniques, including transformer models and graph neural networks (GNNs), are now being explored to further refine subtyping. These methods enable researchers to map complex relationships between biomarkers, genetic predispositions, and environmental factors, creating a more holistic understanding of disease mechanisms.
The impact of AI-driven subtyping extends beyond theoretical improvements—it translates into real-world advancements in drug development. AI-powered subtyping helps mitigate issues of expensive trial failures by identifying patient groups more likely to respond positively to a given treatment, also improving drug efficacy assessments.
As AI capabilities evolve, patient subtyping will become even more precise, leading to adaptive clinical trials that can adjust in real-time based on ongoing patient responses. This shift represents the next frontier in precision medicine—where treatments are no longer designed for the “average” patient but are instead tailored to individuals based on deep, AI-driven insights.
Clinical Trials, Reimagined
With AI and ML-driven patient subtyping, those subgroups can be identified from the start, ensuring that trials focus on the right patients from day one. Here are some other benefits to drug developers:
Comprehensive Cost-Benefit Analysis
Implementing AI-driven patient subtyping can help reduce overall clinical trial costs in the clinical research phase by automating manual tasks and performing rapid statistical analyses, improving efficiency and leading to faster drug development. By accurately identifying patient subgroups likely to respond favorably to treatments, pharmaceutical companies can design more targeted trials, thereby reducing the number of participants required and shortening trial durations. Precision accelerates time-to-market for new therapies, enhancing overall profitability. For instance, the use of precision diagnostics has been shown to reduce unnecessary diagnostic testing and scanning, leading to substantial healthcare savings.
Navigating Regulatory Landscapes
Compliance with regulatory standards is crucial when integrating AI into clinical trials. In the United States, the Food and Drug Administration (FDA) has developed an action plan for AI/ML-based software as a medical device, emphasizing the need for tailored regulatory frameworks, good machine learning practices, and real-world performance monitoring. Similarly, the European Union’s General Data Protection Regulation (GDPR) imposes strict guidelines on data privacy, impacting how patient data can be utilized in AI applications. Adherence to these regulations ensures that AI implementations are both ethical and legally sound.
Ensuring Data Quality and Mitigating Bias
The effectiveness of AI models hinges on the quality of data they are trained on. High-quality, representative datasets are essential to minimize biases that could lead to health disparities. Implementing standardized data collection protocols and employing bias detection algorithms are critical steps in this process. For example, precision medicine approaches that consider individual genetic information can prevent harmful drug interactions and increase overall treatment efficiency, thereby reducing biases in treatment outcomes.
What’s Next?
Patient subtyping is still evolving, but the trajectory is clear. As AI capabilities improve and access to high-quality data expands, subtyping will become even more precise. The integration of multi-modal data—genomics, imaging, and continuous health monitoring—will further refine how we classify patients.
It helps build a future where clinical trials are no longer designed around broad, catch-all patient groups but instead tailored to individuals. Where adaptive, real-time subtyping ensures patients get treatments that actually work for them. And where drug development is faster, cheaper, and more successful.
Modernizing patient subtyping is the future of clinical research. The industry is shifting, and companies that embrace this change will be the ones that lead the next generation of precision medicine.
Author info
Tanmay Sengupta and Ashika Kudiri are Business Unit Heads at Mu Sigma who partner with Fortune 500 Life Sciences companies to prepare for an algorithmic world with a Continuous Service as a Software approach.
