Artificial intelligence (AI) in Clinical Development
The traditional clinical trial process is lengthy, complicated and hindered by its dependence on manual effort and inefficient practices.
Extensive manual effort is needed to gather and analyze data, which is often fragmented between disconnected systems. Moreover, current patient recruitment, enrollment, and monitoring require frequent and often invasive and inconvenient visits to trial sites.
This dependency on in-person sites, in addition to the disparity of clinical trial accessibility for underrepresented populations, negatively affect the flow, enrollment rates and diversity of clinical research. As a result, it currently takes 10 to12 years for a new drug to reach the market.
By leveraging artificial intelligence (AI) and machine learning (ML), clinical research organizations (CROs), researchers and pharmaceutical companies have begun to transform the landscape of clinical research. AI can be used to overcome extant challenges and fuel enhancements in clinical development such as:
1) Optimization of study design: AI can be used to assess and select optimal primary and secondary endpoints, suggest ideal country and site strategies, enrollment models, etc.
2) Site identification, patient recruitment and clinical monitoring: AI can identify trial sites with high recruitment potential that are most likely to meet enrollment targets. It can also accelerate recruitment by suggesting the best strategies for each setting. AI-generated analytics can also provide holistic risk assessment of sites and improve clinical monitoring.
3) Automation of data management processes and improvement of data quality: AI-powered tools can be used for the automatic interpretation of data, seamless integration of multi-system data flow and auto-population of reports and analyses. AI can also facilitate the integration and review of the massive amounts of data usually subject to manual analysis in pharmacovigilance, providing new levels of insight and proactive analytics to enhance quality and oversight.
4) Improvement of patient care: AI-powered algorithms can be used to improve the recruitment of treatment-naïve/undiagnosed patients by analyzing medical information and identifying patients likely to develop a disease. AI-enabled study design can also integrate patient-centric elements aiming to reduce patient burden, improve study efficiency, and reduce the reliance on physical/in-person trial sites; by integrating AI algorithms and wearable devices, researchers can remotely monitor patients’ vital signs and other relevant clinical measures.
5) Enhancement of diversity and accessibility: The integration of AI and wearable devices can also improve the accessibility of clinical trials by reducing the need for travel to a physical site. As a result, the diversification of patients participating in clinical trials becomes more feasible and patient retention/adherence becomes more likely.
6) Acceleration of clinical development: AI can reduce the cost and time needed to analyze clinical trial data, allowing organizations to conduct trials at a higher speed and with a lower cost.
AI has vast potential with deep implications in the healthcare sector. The integration of AI in clinical development processes offers the possibility of faster drug development, automated data flow optimization, unprecedented data integration and data analysis, all without sacrificing patient-centricity. With AI, CROs, researchers and pharmaceutical companies can deliver medicines faster with the potential to change or even save patient lives.