Mount Sinai has announced the 2021 launch of a dedicated AI research center that will study how the technology can enhance the quality of care patients receive and improve the outcomes they achieve.
With worldwide spending on the technology expected to reach $35.8 billion this year, artificial intelligence (AI) is expanding its influence across nearly every industry. The healthcare industry’s adoption of AI has been comparatively slow, but leaders like Mount Sinai, Cleveland Clinic, and New York-Presbyterian have made important strides in the past few years.
From drug design to medication adherence, AI has the potential to revolutionize clinical trials. To advance the development and implementation of this technology, Mount Sinai’s Icahn School of Medicine has announced plans to launch a new center for AI research in 2021. The Hamilton and Amabel James Center for Artificial Intelligence and Human Health will focus on genomics, disease modeling, imaging technology, and prediction of novel therapies.
This announcement reflects a larger industry trend of incorporating AI into clinical practice with the goal of enhancing treatment, reducing costs, and improving patient outcomes. Here’s what sponsors and CROs need to know about Mount Sinai’s plans, and how they are likely to impact AI adoption in clinical trials.
Mount Sinai Commits to AI Research
“We see the potential of artificial intelligence to radically transform the care that patients receive, and we want to shape and lead this effort,” says Kenneth L. Davis, MD, President and CEO of the Mount Sinai Health System.
The Hamilton and Amabel James Center for Artificial Intelligence and Human Health hopes to use AI and machine learning to improve the diagnosis and treatment of complex diseases such as cancer, Alzheimer’s, and diabetes. Researchers plan to use the technology to develop novel therapies and more accurately predict patient outcomes.
Mount Sinai’s announcement arrives on the heels of other recent developments in AI research. In March 2019, the Cleveland Clinic announced the launch of its Center for Clinical Artificial Intelligence, where researchers are developing machine learning models to improve quality of care and clinical outcomes. Similarly, NewYork-Presbyterian created its own offsite AI center to monitor patients and reduce fatigue in healthcare providers.
The Growth of AI in Clinical Trials
Mount Sinai’s investment and similar initiatives undertaken by other healthcare leaders are likely to accelerate the adoption of AI in clinical trials. This technology has the potential to save researchers time and money, while making care more accessible to patients. AI can improve processes from data management to patient onboarding, as well as spot errors or irregularities with great efficiency and effectiveness.
The technology is especially valuable when it comes to drug discovery and development. AI can improve the drug design algorithms that are used to predict the safety and efficacy of a treatment. Such improvements will lead to shorter drug development timelines and more innovative approaches to research. Plus, as a 10% improvement in predicting drug efficacy could lower development costs by a remarkable $100 million per drug, implementing AI technology is likely to lead to substantial savings for sponsors and CROs.
Finally, AI provides significant benefits for patient recruitment and screening. Sponsors and CROs can use AI applications to match qualified patients with relevant clinical trials. Machine learning can also help researchers predict which patients are most likely to accept an invitation to join a trial. In this way, AI improves efficiency for sponsors and CROs, and helps patients avoid the inconvenience and disappointment of being turned away from a trial.
While there are certain financial and regulatory hurdles that must be cleared when implementing AI in clinical research, the benefits of doing so are far-reaching and substantial. By all accounts, as AI continues to mature and becomes increasingly widespread, it will enhance trial design and improve the patient experience.