A crucial component of the pharmaceutical industry’s drug discovery process is clinical trials. Clinical trials are essentially research projects that check the safety and efficacy of a medical procedure or equipment on humans. Nowadays, the use of artificial intelligence in clinical trials is crucial. The pharmaceutical medicine market has seen some changes, but it is still a lucrative one. According to Quintiles IMS Holding, global prescription medicine spending is anticipated to reach close to $1.5 trillion by 2021.
Real-world data is the term for the growing amount of scientific and research data that biopharmaceutical businesses have access to from a number of sources in recent years (RWD). However, they frequently lacked the knowledge and tools necessary to use this data effectively. A faster understanding of diseases, the identification of potential patients and important investigators to guide site selection, and the support of unique clinical study designs can all be achieved by unlocking RWD utilizing predictive AI models, artificial intelligence in clinical trials, and analytics tools.
Cleansing, aggregating, coding, storing, and managing the ongoing stream of clinical trial data may be made possible by artificial intelligence in clinical trials algorithms and a strong digital infrastructure. Additionally, enhanced electronic data capture (EDC) must be able to minimize the effects of human error in data collecting and promote seamless database integration.
Here are 3 top uses of artificial intelligence in clinical trials;
1. Clinical Trial Design
A variety of tactics are being used by biopharma businesses to innovate trial design. Trial design has been energized by the growing body of scientific and research data, including information from ongoing and completed clinical trials, patient support initiatives, and post-market surveillance. AI-enabled tools can collect, organize, and analyze the growing quantity of data produced by clinical trials, including unsuccessful ones, and can uncover useful patterns of data to aid in design.
Artificial intelligence in clinical trials is a key component of clinical trial design using in clinical trials, situated in San Diego, California, and was established in 2015. AI claims to use NLP to manage clinical trial operations more effectively for researchers. the Trials-running algorithms. Apparently trained on “billions of data points from historical clinical trials, medical journals, and real-world sources,” AI’s cloud-based Study Optimizer tool identifies risk variables and makes suggestions for a clinical trial modification. For instance, the procedure starts when a user uploads their Microsoft Word or PDF study protocol materials to the site.
The protocol text is examined for risk factors, and any potential trial-related obstacles, such as recruitment and retention, are displayed on a dashboard. The platform offers suggestions for risk mitigation to optimize the study protocol based on the insights gained from the analysis. On the platform, several team members can work together to guide stakeholders through their trials. This promotes communication among the team members.
2. Patient Monitoring, Medication Adherence, And Retention
By automating data collection, digitalizing common clinical assessments, and sharing data across systems, AI algorithms can aid in the monitoring and management of patients.
Combining wearable technology with AI algorithms can improve engagement and retention by enabling continuous patient monitoring, real-time insights into the safety and efficacy of therapy, and the prediction of dropout risk.
3. Clinical Trial Optimization
Brite Health has achieved significant advancements in the optimization of Clinical Trials using artificial intelligence. Brite Health, a California-based company founded in 2015, says it uses machine learning to better manage patient engagement in clinical trials. The company’s technology, which purportedly received training on millions of clinical data points, powers the patient app and site dashboard. The suggestion engine can spot important indicators that frequently co-occur with patients dropping out of research projects. For instance, the system notifies the user of their scheduled duties and site visits throughout the clinical trial via the mobile patient app (shown below).
Additionally, the app’s handpicked content and tailored communications promote user engagement. Patients have 24/7 access to a conversational chatbot that has been configured with approved research materials. The dashboard of the software allows research teams to track patient involvement and adherence (shown below). The recommendation engine’s insights and alerts enable earlier patient intervention to stop nonadherence and study dropouts.
Case studies and the overall amount of money raised throughout our investigation were not found. However, Plug and Play Ventures and Unshackled Ventures are some of the current backers. Brite has taken part in Bayer’s Dealmaker initiative and a Pfizer-led Health and Wellness Innovation Program. There don’t appear to be any C-level executives on the team with experience in artificial intelligence in clinical trials. We advise readers to be cautious of businesses that make AI claims yet do not have any C-level AI experts on staff.
While enhancing productivity costs and clinical development outcomes, artificial intelligence can shorten the duration of clinical trials. Our series on the effects of AI on the biopharma value chain continues with this third study. Artificial intelligence in clinical trials can be used to identify diseases, provide healthcare services, and even develop new treatments, all while improving clinical trials. The scale and speed of AI are significantly superior to any system that depends just on human activity, however, it may also be restricted to a few jobs.
Given that we have not yet reached a point where AI can operate fully without human contact, this will undoubtedly be the biggest difficulty moving forward. In other words, healthcare organizations will need to motivate people and establish a system that strikes a balance between their needs and those of the general population.