Ryght Unveils Global Network to Enhance Clinical Trials with AI
Ryght, a generative AI platform transforming clinical trials by automating feasibility assessments, protocol development, and document creation. Its newly launched “Ryght Research Network” connects researchers, sponsors, and AI tools globally to streamline collaboration and advance medical research.
Clinical Trials: A Challenge-Filled Landscape
Clinical trials are essential for testing new treatments and therapies, providing the scientific evidence needed to advance healthcare and improve patient outcomes.
However, they are fraught with challenges. Recruiting study participants is one of the most difficult aspects of the clinical trial process and is widely regarded as one of the biggest obstacles to success. These processes are also costly, and require significant time, and expertise of various professionals.
In recent years, the healthcare landscape has become increasingly complex, driven by the COVID-19 pandemic, shifting global economies, and evolving patient needs. This has impacted the clinical research workforce, which has endured attrition, shortages, and staffing issues, with approximately 30% of Clinical Research Associates leaving their roles in the U.S. The obstacles of managing complex trials, coupled with long hours and high stress, have made these roles increasingly difficult to sustain.
Generational health concerns, shaped by lifestyle, environmental factors, and technological advancements, further highlight the need for healthcare systems to adapt to diverse and evolving demands.
The Ryght Research Network: A Potential Game-Changer for Clinical Trials
The Ryght Research Network aims to tackle the challenges that currently hinder clinical trial processes, including complex protocols, staff burnout, and operational inefficiencies.
It plans to transform the way trials are conducted by automating critical tasks such as feasibility assessments, site selection, and patient referrals while enabling real-time communication between sites and sponsors. Ryght has the potential to speed up the selection of optimal sites and patients for sponsors.
The Ryght Research Network opens its first U.S. academic site at the Keck School of Medicine of USC (CA, U.S.). Together, they plan to use its network to focus on patients affected by cancer, with the initial phase focusing on ongoing clinical trials at Keck Medicine’s USC Norris Comprehensive Cancer Center.
“We share the same vision with the Keck School of Medicine in aiming to advance clinical research by expediting trial processes to improve the outcomes of all patients affected by cancer. Leveraging AI technology and innovation can help us accomplish that in a timely manner.”
Chadi Nabhan, MD, MBA, Chief Medical Officer and Head of Strategy at Ryght.
“This partnership enables Keck School of Medicine of USC to be at the forefront of innovative, efficient research, helping bring therapies to patients faster and supporting our mission to improve global health outcomes”.
David Friedland, M.D, Ph.D., Associate Dean of Clinical Research, Keck School of Medicine of USC.
In addition to optimizing the clinical trial process, the Ryght Research Network will also link USC researchers with new clinical trial and sponsorship opportunities, including a portal for exploring available trials and the possibility of developing customized AI-driven tools.
Improved clinical trial efficiency could enable the medical school to conduct more trials, enrol more patients in each trial, and diversify the range of trials it offers, ultimately elevating the standard of patient care.
Potential Risks and Challenges in Optimizing Clinical Trials with AI
While the potential benefits of the Ryght Research Network are significant, there are also risks and challenges that should be considered.
If an AI model is trained on biased or unrepresentative data, it could overlook key patient groups or misinterpret trends, ultimately affecting the validity of the research and the applicability of the results to a broader population.
For example, biased AI algorithms driving mismatched patient enrolment could result in the selection of patients in clinical trials who are not ideal candidates, jeopardizing their safety and distorting outcomes.
Given that the Ryght Research Network relies on large-scale data collection and real-time communication between sites and sponsors, ensuring compliance with data protection regulations is critical to safeguarding patient confidentiality.
Additionally, regulatory challenges may arise, particularly in ensuring consistency across different regions and jurisdictions. This could complicate the approval process for trials, as varying standards and requirements could create delays or additional hurdles for the AI-driven site selection and patient matching processes within the network.
Therefore, while the Ryght Research Network offers promising advancements, it will be crucial to balance innovation with careful oversight to mitigate these potential downsides.