AI-based Digitalized Clinical Trials – The In-Silico style
For the healthcare sector, clinical trials cost billions of euros and often proceed for years without any guarantee of success after the study. It is a method to assess a drug or medical device’s effectiveness in a clinical setting but is very expensive due to complicated clinical protocol and strict record-keeping. Artificial intelligence techniques are increasingly being tested to remove some of these heavy organizational liftings and have resulted in many In-silico experiments.
The term In-silico, which in Latin means ‘in silicon,’ was coined in 1987 as an expression denoting biological experiments conducted on a computer or through computer simulation and was first used in 1989. In-silico tests and virtual pharmacological therapy take advantage of human-based modeling and simulation technologies. This methodology has been used for modeling and simulation in both the pre-clinical trials and clinical evaluation of a future medical device since its inception, taking this broad spectrum of applicability into account.
Virtual Patients – Will patients be replaced by computers?
Many startups are trying to digitize medical research using big data and the internet of things. AI healthcare startups are providing platforms to apply machine-learning algorithms to automate clinical trials. Boston based TriNetX is one such company that offers services around clinical trial design and patient feasibility. Its software collects and analyzes patient health records to identify ideal candidates for clinical trials. TriNetX enables researchers to analyze the patient population and perform ‘what-if’ analyses in real-time, thereby developing virtual patient cohorts that can be re-identified for potential recruitment into clinical trials. And all this can be done in minutes!
The clinical analytics platform Saama from Silicon Valley uses its Life Science Analytics Cloud to seamlessly integrate, curate, and animate unlimited sources of structured, unstructured, and real-world data to deliver more actionable insights. Saama aligns key stakeholders who are either part of, or involved with, clinical research teams, thereby providing its sponsors and CROs services such as data aggregation for faster decision-making and more effective collaboration.
Irish startup Teckro simplifies clinical trials by using machine learning to digitize paperwork, enabling researchers to collaborate on smartphones from anywhere. This company provides a mobile platform that allows clinical researchers to quickly retrieve information and control various clinical protocols using the portal.
Another startup is Owkin that helps companies design better clinical trials using AI. Owkin’s platform, Socrates, uses machine learning to integrate biomedical images, genomics, and clinical data, among other sources, to discover biomarkers and mechanisms associated with diseases and treatment outcomes. A recent publication in Nature Medicine by Owkin reports a deep-learning program trained on 3,000 patients with an aggressive form of lung cancer that enabled the company to develop a predictive model for lung cancer.
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