AI in Clinical Trials: Enhancing Efficiency and Reducing Costs

Clinical trials are the most critical phase in the drug development process. They assess the efficacy and safety of a drug, clinical approach, or therapy. Clinical trials are expensive, labour-intensive, and complex processes prone to errors and biases. Pharmaceutical companies must individually select and recruit volunteers for clinical trials, which is costly and time-consuming. 

However, artificial intelligence (AI) transforms how pharmaceutical companies conduct clinical trials. AI can be used to select clinical eligibility criteria, identify ideal clinical trial candidates, and reduce sample size requirements. This article discusses how AI-powered clinical trials can reduce costs and enhance efficiency.

How is AI transforming clinical trial processes?

AI can transform various aspects of clinical trials. Its aims include enhancing efficiency and reducing the costs of clinical trials. Here are some of the steps where AI in clinical trials can be used:

Patient selection and recruitment

AI algorithms can be used to sift through enormous databases of patient records. The desired criteria can be input into the algorithm, providing a list of patients who match the eligibility criteria. This accelerates recruitment and ensures that the patient is suitable for the study.

Patient retention

Patients often drop out of clinical trials, which poses challenges for researchers because it can create problems in data analysis. AI in clinical trials can be used to determine which candidates are likely to drop out. By providing patients with regular surveys and feeding this information into the algorithm, AI can predict which candidate might be at risk of dropping out. AI can also offer solutions and strategies to improve patient retention.

Data management and analysis

During clinical trials, data is collected from various sources such as electronic health records, wearable gadgets, medical devices, etc. Collection, integration, and analysis of such vast databases are time-consuming. In clinical trials, AI can handle complex databases and perform sophisticated analyses, saving time and money.

Optimise trial design

AI simulations can predict various scenarios and outcomes of clinical trials. Based on these results, the design of clinical trials can be optimised. Furthermore, potential trial challenges can be predicted, and solutions can be designed beforehand.

Predictive modeling

AI can also estimate the time and cost of clinical trials. The clinical trial design can be input into the algorithms along with additional parameters like number of participants. Based on this, the algorithm can estimate time and cost. The design can then be adapted to optimise both parameters

Key AI-powered technologies used in clinical trials

Although AI in clinical trials is still nascent, it has already been integrated into some steps of clinical trials. Here are some critical AI-powered clinical trial technologies:

• Trial Pathfinder is an open-source AI tool that uses electronic health records (EHRs) to simulate clinical trials. EHR data is integrated to understand how adjusting the inclusion criteria of the clinical trial can change the overall survival risk ratio. 
• TwinRCTs is a clinical trial software that combines AI, novel statistical methods, and digital twins (a virtual model that can accurately reflect a physical object) to improve the success rate of clinical trials. The model can predict disease progression in clinical trial patients using external cohorts.
• Criteria2Query is an AI tool that allows users to input inclusion and exclusion criteria. The tool uses a web-based interface to find matching candidates inpatient databases.
• TrialGPT can prompt a large language model to find appropriate clinical trials for a patient. A description of the patient is input, and the model determines whether the patient is eligible for any clinical trials, providing reasoning for its results.
• Deep6 AI: Deep6 uses AI in clinical trials to match ongoing or upcoming studies with patients. It analyses real-time unstructured clinical data to connect interested patients with relevant studies.
• Trials.ai: Trails.ai employs an AI algorithm that streamlines the design and management of clinical trials. Researchers can analyse the cost and time associated with the design and optimise to ensure both remain within the desired limits.
• IBM Watson Health: IBM Watson Health uses AI to analyse large databases of clinical trials to accelerate drug discovery.

Challenges in implementing AI in clinical trials

Despite some of the fantastic real-world applications of AI in clinical trials, the use of AI in clinical trials still faces some challenges, as discussed here: 

Stringent regulatory requirements

AI algorithms need access to real-time, high-quality, and vast data to predict trial outcomes, select correct patients for trials, and monitor patient health. However, considering the strict regulatory guidelines on data privacy and informed consent, these data may not always be available, which hampers the algorithm's efficiency.

Expensive

The implementation of AI in clinical trials is expensive. Companies must hire experts to integrate the robust infrastructure and computational resources (i.e., the database). This is a time- and labour-consuming process that turns out to be expensive. Furthermore, existing practices and systems must be integrated into AI to ensure it is more efficient than the traditional process.

Transparency and interpretability

Most AI models operate as black boxes, which means they do not provide any explanation for their recommendations or decisions. The lack of transparency and interpretability is problematic because, in clinical settings, each decision needs to have a logical reason. Furthermore, AI models that do not provide reasoning or are not transparent may not be adopted because researchers will not trust the model.

Training data biases

AI algorithms are trained on existing data, which may have biases. AI systems may inadvertently carry forward these biases, which may not apply to all patient populations, severely hampering their reliability. 

Conclusion

The use of AI in clinical trials has much potential, especially for enhancing effectiveness and reducing costs. AI-powered technologies can optimise data management and analysis, improve patient selection and retention, and optimise clinical trial designs. To this end, pharmaceutical companies have already integrated several AI technologies.

However, AI faces some challenges despite this integration, primarily due to the strict data privacy laws, expensive integration requirements, and poor transparency. As we move into the future, AI in clinical trials must overcome these and many other challenges to be widely adopted. However, considering its advantages, AI in clinical trials has vast potential. 

FAQs

1. What are the ethical concerns with using AI in clinical trials?

Ethical concerns with using AI in clinical trials revolve around informed consent, data security, and patient privacy. Furthermore, AI may unknowingly adopt biases based on the data on which it is trained.

2. Are there any specific regulations on AI in clinical trials?

Yes, there are specific regulations imposed on AI. It is also subject to all data security, privacy, ethical rules, and guidelines applicable to all pharmaceutical companies.

3. Can AI be used to predict clinical trial outcomes?

Yes, AI can be used to predict outcomes. This allows researchers to make informed decisions during trials.