Using Artificial Intelligence to Improve Clinical Trial Design and Execution

Artificial Intelligence (AI) is a rapidly growing field of science and engineering that has the potential to revolutionize the way clinical trials are designed and executed. AI can be used to create a control group based on patient-level data from previous clinical trials and on real-world data sets such as virtual controls, allowing for more accurate estimation of the effect of treatment through early-stage trials. AI can also be used to reduce the total number of trial participants, thus accelerating testing. AI offers a variety of tools that can be used to improve the design, planning, and execution of clinical trials.

Machine learning, natural language processing (NLP), and optical character recognition (OCR) can be used to link large and diverse data sets, such as electronic medical records (EMR), published medical literature, and clinical trial databases, in order to improve patient selection criteria. AI techniques, in combination with portable technology, can also be used for efficient, real-time, and personalized monitoring of patients during the trial. In addition, AI technologies such as natural language processing can be used to extract tens of thousands of new clinical data (symptoms, diagnoses, treatments, genomics, lifestyle data, etc.) from fragmented medical documents. AI tools are also an excellent way for companies to start their clinical trials quickly by efficiently identifying patients who belong to specific groups before performing aptitude assessments with them.

AI can also be used to determine and choose the optimal primary and secondary endpoints in the study design in order to ensure that protocols most relevant to regulators, payers, and patients are defined by huge sets of healthcare data. The transition to virtual clinical trials means that any suitable participant who wants to participate in medical research and meets the criteria can do so. AI is gaining recognition on a large scale as support for decentralized trial designs, allowing for patient-centered clinical trial designs. In virtual trials, patients can be enrolled in real time and in their usual environment (rather than just in controlled clinical environments) and monitored remotely. Data collection and management based on AI can change the rules of the game for life science companies in the drug development process. AI can help a company monitor a clinical trial by collecting and analyzing data in real time. In conclusion, AI is an invaluable tool for improving clinical trial design and execution.

It offers a variety of tools that can be used to link large and diverse data sets, extract new clinical data from fragmented medical documents, identify suitable participants quickly, determine optimal primary and secondary endpoints in study design, and monitor patients during the trial. As such, it is no surprise that 76% of respondents in Deloitte's digital innovation in life sciences survey are currently investing in AI for clinical development.

Deanna Trueman
Deanna Trueman

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