Authors: Priyanka Sharma, Vice President, Software Engineering and Andrew McVeigh, Chief Architect
Drug development is difficult, expensive, resource-intensive, and often unsuccessful. The overall success rate of trials is only 7.9%.1 Termination of pharma innovations in the R&D pipeline doubled in 2022 compared to the previous year, and an analysis of registered trials from 2000 to 2019 revealed a steady decline in completed drug trials over the decade.2,3
While clinical trials experienced a boom during the pandemic, a 2023 report by Deloitte indicates that the effects are temporary as R&D returns are again on a decline. In fact, 2022 marked the lowest number of novel drug approvals by the FDA since 2016.4 This raises concerns for the future of public health, especially when combined with proposed funding cuts to the NIH, NCI, and CDC.5
Given that it takes more than $2.6 billion and well over a decade to bring a new prescription drug to market, the ramifications of declining trial success may not be felt for years.6 The good news is that overall, despite the recent downturn, registered studies have increased over the years.7 However, a pressing question remains: what can we do to make clinical trials more viable and accessible?
With the promise of faster processes at a reduced cost, the potential of artificial intelligence (AI) in clinical trials cannot be ignored. This disruptive innovation has the potential to positively impact study efficiencies that may prove transformative for the trial process and patient health outcomes in life-changing areas like oncology, infectious diseases, mental health, and many other critical areas.
Clinical trials still have many opportunities for automation, and AI can play a role in this to help accelerate the drug discovery process and reduce costs by:
While AI’s capabilities undeniably amplify trial efficiency, the symbiotic partnership between the technology and uniquely human qualities such as critical thinking, creativity, and empathy is where clinical trials stand to benefit most.
By taking over repetitive tasks from humans, AI can free up study teams for more high-value, specialized tasks. AI can also function as augmented intelligence, providing data that helps researchers improve treatment plans and optimize resources. The deep domain and pharmaceutical expertise of humans—understanding global regulatory environments, patient behavior, and therapeutic knowledge—is necessary to add context to interpreting AI’s data and work for optimal results. For example, researchers from the NYU School of Medicine and the NYU Center for Data Science found that combining AI with analysis from human radiologists significantly improved breast cancer detection.12
Using AI augmentation in clinical trials can also help study teams analyze and measure unstructured data gathered from patient consent forms and clinical outcomes. These data can come in various formats, including comments on a patient’s experience. As AI still struggles with open-ended inference, human context and critical thinking are still needed for interpretation.13 AI-generated trial designs and documents, such as patient consent forms, also need human verification to ensure validity.
We’re currently exploring these opportunities at Suvoda as we work on prototypes to analyze informed consent documents to improve patient comprehension. We’re also trialing AI as an accelerator for study design with guard rails and reviews as part of the process. In this context, AI reads the protocol, parses it, and sets trial parameters. Design consultants can then review these parameters, which are crucial for a study implementation. The building and success of this tool involves human experts in multiple disciplines, from technology to science and clinical operations.
Regardless of AI supplementation, human connection, real-life experience, and empathy will always be essential elements in the care of patients to elicit therapeutic change. Empathy plays a crucial role in the connection between care providers and their patients. In clinical trials, participants take on considerable risk, hoping that these trials will benefit not only themselves but also provide insights to enhance health outcomes for others. Acknowledging their commitment and the time they invest is essential to honor their altruism. This human element is vital for the successful execution of clinical trials.
When AI, human expertise, and technology work together, it can greatly improve the experience of those involved in clinical trials—both study participants and researchers. This has the potential to lead to more successful studies at reduced cost. It is a win-win situation, where everyone from sponsors, CROs, sites, and patients benefit from optimized workflows in clinical trials. However, much like technological innovations before, a practical approach to AI is essential to maximize its potential while minimizing the stresses of trial management and participation for study teams and patients.
Finding the right balance will ensure that AI helps us make trials more efficient and effective while also prioritizing the safety and care of the people involved. The collaboration between AI and human insights could completely change how we approach clinical trials, bringing new levels of efficiency and innovation to clinical research. It’s an exciting time and one that we are meeting with measured optimism and a commitment to purpose-built, tested solutions that advance life-sustaining therapies for those in need.
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