How an AI Boost Benefits in Clinical Trials

Leveraging AI can expedite and enhance patient care at screening, diagnosis, and treatment, including better patient journey management.

FREMONT, CA: AI-based capabilities, including data integration and interpretation, pattern recognition, and evolutionary modeling, are vital to gather, normalize, analyze and harness the increasing masses of data that fuel innovative therapy development. Indeed, AI and modern analytics were viewed as the digital technology with the most potential to enhance clinical R&D productivity in the digital disruption in the biopharma sector. AI has several potential applications in clinical trials. AI technologies make possible innovations vital for transforming clinical trials, like seamlessly coupling clinical trials, developing new patient-centered endpoints, and collecting and analyzing data.

The fact that there are several drugs in clinical development, comprising some targeting the same or similar indications, also releases new levels of competition both for patients to participate in clinical research and commercialization. These development programs follow the long-established method of selecting and targeting cells with higher proliferation activity connectivity with the disease. Manufacturers have focused clinical research on targeting the root cause of disease to offer an optimal and potentially curative advantage. This shift has also introduced the requirement for more advanced protocols in patient screening and the execution of clinical research.

Firms are leveraging options, including innovative neural networks and Bayesian algorithm-powered software systems to mine datasets of patient information to find enrollment candidates. Many are also leveraging AI to deploy predictive models to enhance the statistical significance of data collected from more targeted candidates and help mitigate trial costs.  AI tools can enhance the potential to assess which patient candidates might respond better to drug treatment and predict their trial dropout rates.

Latest AI tools also enable drug manufacturers to better assess issues related to drug administration and optimal dosing regimens. Customized algorithms can decide the impact of various dosing levels and schedules on drug efficacy and safety, which could ultimately mitigate the risk of adverse events, trial delays, and patient discontinuations. Most stakeholders expect that the usage of AI and innovative data analytics in drug development will continue to widen in the years ahead, with significant implications for the speed, efficiency, and costs of programs.

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