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.

Discover the leading AI companies in clinical trials

Clinical Trials Arena has listed some of the leading companies offering products and services related to AI using its intel, insights and decades-long experience in the sector.

The information provided in the download document is drafted for clinical trial executives and technology leaders involved in AI innovations.

The download contains detailed information on suppliers and their product offerings, as well as contact details to aid purchasing or hiring decisions.

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-focussed. 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.