Technology has transformed clinical trials over the last two decades, making it much easier to gather and analyse data, assess results and ensure the safest and most effective outcome.
In particular, AI and machine learning have become integral to clinical trials, improving efficiency, accuracy and the likelihood of success.
But how are these evolving technologies being applied and how is this transforming the way clinical trials are being conducted?
Well, that’s what we’re here to discuss.
Optimising trial design and predicting the outcome
Designing clinical trials is complex and an inefficient design can lead to poor outcome prediction and increases the risk of the trial failing.
The good news is synthetic data and AI models can be used to simulate trial scenarios and predict which design might yield the best results. This can help to reduce costs and the need for larger populations in the early phases of the trial.
For example, AI can be used to create virtual patient cohorts. This is done using historical data to model patient responses without exposing real patients to potential risks.
Not only this, but machine learning models can also be applied to predict patient outcomes based on historical medical data and patient profiles. This enables you to optimise the study design, endpoints and dosage or treatment to improve overall success rates.
Making personalised and adaptive trials possible
Traditional clinical trials are typically designed with a one-size-fits-all approach, and in many cases, this can overlook personal responses to treatments or medication. However, machine learning allows trials to adopt a more personalised and adaptive approach.
By segmenting patients based on genetic, biological or other behavioural characteristics, these tools make adaptive trials possible. This means that the trial design will evolve based on real-time data, allowing for early adjustments.
For example, AI can continuously analyse incoming data from the trials to suggest modifications, such as increased dosage or a different type of treatment. This enhances precision and increases the likelihood of a successful trial.
Enabling decentralised and virtual trials
Traditional clinical trials often require patients to visit centralised locations, therefore, they must be local to the trial centre or willing to travel. This can be inconvenient and can reduce patient participation.
However, AI and machine learning can now facilitate decentralised or virtual clinical trials. This design allows patients to participate remotely, for example, through wearable devices and mobile health apps.
ML algorithms can then be applied to monitor patient data in real time, making it possible for these trials to occur without the need for patients to travel or physically be on-site.
Selecting and recruiting participants
Finding suitable participants for clinical trials can be time-consuming and expensive but if you don’t get this right, it’s a waste of time and resources. Plus, the trial is likely to fail.
However, algorithms can be applied to analyse large datasets (such as patient health records) in order to identify those who meet the specific criteria for trials. For example, natural language processing (NLP) can help to find potential candidates much faster than manual screening.
This speeds up the recruitment process and improves the chances of finding patients who are more likely to respond to the drugs or treatment being tested.
On top of that, machine learning models can be used to predict patient behaviour and the likelihood that they’ll not only participate but complete the trial. This increases participant retention rates.
Collecting, analysing and managing data
Clinical trials generate a vast amount of data, and manually processing and analysing this data can be very time-consuming, not to mention that there is an increased risk of human error.
But using AI-driven platforms, we can now automate data entry, processing and analysis. These tools help to ensure higher accuracy and faster results.
Another way in which this technology can be applied is by using machine learning models to detect anomalies in the data as early as possible. This is crucial to prevent costly errors and ensure that all data gathered is of the highest quality.
Monitoring and improving patient adherence
While participants may be willing to take part, they don’t always accurately and completely adhere to the treatment protocols set out. For example, they may forget to take their medication on several occasions. This can lead to inaccurate trial results.
However, new wearables and sensor technology, combined with AI tools, can help to monitor patients in real-time, tracking their adherence to medication schedules, lifestyle changes and more.
This data can then be processed through machine learning algorithms enabling those conducting the study to identify any patterns or deviations. This makes it possible for them to intervene quickly and get the study or participant back on track.
Reducing the cost and time of clinical trials
Clinical trials can be long and expensive and unfortunately, many have a high risk of failure. However, by using AI and machine learning, researchers can optimise patient selection, improve adherence and use predictive analytics to drastically reduce the time and cost involved.
This is because these tools can narrow down the list of compounds to those most likely to succeed. This can cut down on wasted time and resources in the earlier phases of trials and quickly reveal whether it’s worth continuing or not.
These tools also make it easier to gather and analyse data, cutting down on the need for manual work.
Gathering real-world evidence
Finally, after a new medication, treatment or intervention is approved and put on the market, it’s time for the last phase of the trial. This involves gathering real-world evidence to understand the efficacy and safety of a new drug or treatment, as well as rare or long-term side effects. The trouble is, this can be costly and difficult to track.
Using AI tools, researchers can mine real-world data from healthcare systems, wearable devices, social media and more to gather ongoing feedback on a drug or treatment’s performance.
This ensures that adverse effects are flagged as early as possible and that ongoing efficacy is monitored more effectively without the need for a huge amount of financial investment and resources.
Also Read: Top 10 Machine Learning Algorithms You Must Know in 2025
