Artificial intelligence (AI) is instrumental in solving several key clinical trial challenges. It can help in the synthesis and analysis of ever-expanding data for the development of innovative therapies. Combined with machine learning (ML), AI can transform the clinical development process, providing significant time and cost efficiencies together with improved and quick insights for better decision-making.
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Amongst the leading suppliers of AI for clinical trials include Medidata, IQVIA, BenevolentAI, Renalytix AI, Prometheus Biosciences, ReviveMed, Insitro, Sensyne Health, Saama, GNS Healthcare.
How is AI improving operational efficiencies in clinical research?
AI is one of the most promising tools for making healthcare more efficient and patient-focused. It helps in trial design, recruitment, behavioural analysis, assisted diagnostics, generating real-world evidence, predictive analytics, and creating medical records. A few examples of the applications of AI and ML in the clinical research process include:
Study design
AI tools can help in evaluating and selecting optimal primary and secondary endpoints in study design, which helps in the optimisation of site strategies and patient recruitment models. An improved study design also helps in increasing the chances of success with more precise planning.
Site identification and patient recruitment
AI in clinical trials can help in the identification of the sites for clinical trials and more appropriate strategies for patient recruitment through patient population mapping and site targeting. This helps sponsors to expedite recruitment and reduces issues such as under-enrollment.
Pharmacovigilance
AI technology addresses several challenges of pharmacovigilance (PV) by automating highly manual processing tasks and offers improved insights and analytics to make the data more usable while ensuring quick identification of adverse events.
Data-driven clinical research
Digital clinical trials can improve medication adherence, remote patient monitoring, decentralised or virtual trials, and digital therapy. AI tools can be used for automated analysis of electronic medical records (EMR) and the databases of clinical trial eligibility to match them with recruiting clinical trials from various announcements of trials or registries.
FAQs
What are the key benefits of AI in clinical trials?
AI offers significant advantages in clinical trials by streamlining processes such as data management, patient recruitment, and trial design. It can quickly sift through large volumes of medical data to identify patterns, making it easier to predict trial outcomes and optimise protocols. AI also reduces human error and speeds up decision-making, which can shorten the duration of trials, lower costs, and improve the quality of findings. Additionally, AI can help generate real-time insights, allowing researchers to adapt trials on the fly based on interim results.
How does AI assist in patient recruitment?
AI plays a critical role in patient recruitment by using advanced algorithms to match eligible participants with appropriate clinical trials. It analyses a vast amount of patient data from various sources, such as medical records, social media, and healthcare apps, to identify candidates who meet the specific criteria of a trial. AI can also predict where eligible participants are likely to be located, enabling more efficient recruitment and site selection. By automating these processes, AI reduces recruitment time and the likelihood of under-enrollment, which is a common issue in clinical trials.
How can AI improve pharmacovigilance?
Pharmacovigilance, or drug safety monitoring, is essential to ensure the safety of medications, and AI is transforming this field by automating the detection and reporting of adverse events. Traditional methods of pharmacovigilance often rely on manual data entry, which is time-consuming and prone to errors. AI can analyse vast amounts of data from multiple sources, including electronic health records, social media, and patient-reported outcomes, to identify safety signals much earlier. By doing so, AI ensures faster reporting of potential risks, enhances patient safety, and helps regulatory bodies make more informed decisions about drug approvals.
What role does AI play in study design?
AI enhances the study design process by using predictive analytics to simulate different trial scenarios, allowing researchers to select the most effective strategies. AI tools help identify optimal endpoints, sample sizes, and study durations based on historical data and real-time trends. These insights lead to more efficient and successful trials, as AI can anticipate challenges such as patient dropouts or delays in data collection. Additionally, AI can streamline protocol amendments, which are often necessary but time-consuming, thus ensuring trials remain on track and within budget.
Can AI help with decentralised clinical trials?
Yes, AI is a key enabler of decentralised clinical trials (DCTs), which allow patients to participate remotely. AI supports remote monitoring by analysing data from wearable devices, smartphones, and other digital health tools, ensuring that trial data is collected in real time without requiring participants to visit a clinical site. This reduces the burden on patients and expands access to clinical trials, particularly for those in remote or underserved areas. AI also improves adherence to trial protocols by sending automated reminders for medication intake or scheduled virtual appointments, ultimately improving data quality and trial outcomes.