In recent years, the use of artificial intelligence (AI) in the pharmaceutical industry has increased significantly. Drug development requires clinical trials, which could undergo a revolution due to Artificial Intelligence.
Clinical research has a vast potential and future for artificial intelligence. AI can help a business in several ways, including patient selection for clinical trials, cohort composition, help with recruitment, and patient monitoring throughout a clinical trial.
AI algorithms could clean, aggregate, code, store, and manage ongoing clinical trial data when used in conjunction with a robust digital infrastructure.
Additionally, enhanced electronic data capture (EDC) should minimize the effects of human error in data collection and facilitate seamless database integration.
The following article will thoroughly examine how AI is used to increase clinical trials’ effectiveness.
What is Artificial Intelligence (AI)?
Artificial intelligence is the intelligence displayed by tech-based and machine-based machines and algorithms. It is not the same as human intelligence or feelings, but rather, it is more like how humans use algorithms to solve problems.
Advanced analytics systems like machine learning and deep learning enable the process of automation in which artificially intelligent machines continue to perform their tasks by getting fuel from unstructured data, images, and texts in the system inputs; such devices and systems are significantly more intelligent.
The functions of artificial intelligence systems can range from single, straightforward tasks for some to more complex, human-like processes for other types of intelligence systems, depending on their power and level.
What exactly are Clinical Trials?
Any field’s dynamics rely heavily on research and invention. To advance with new medications, therapies, and treatments for pre-existing and rapidly evolving diseases, however, such studies and trials are conducted in the field of medical sciences.
Clinical trials and observational studies are two different categories of research. In observational studies, volunteers for medical causes are observed using techniques such as physical examinations, medical tests, and questionnaires about various topics to gain insight into their lifestyle and cognitive health.
However, in clinical trials, research is conducted to understand better the medical assistance that might be offered in cases or diseases that have been identified. They come to conclusions that enable the researchers to explore the potential for novel therapies, medications, and medical equipment to help with the ongoing medical condition.
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Therefore, observational studies are the foundation for clinical trials.
5 Ways Artificial Intelligence can Help in Clinical Trials
Clinical trials can be revolutionized with improved success in attracting, engaging, and retaining committed patients throughout the study and, after analysis, termination using AI-enabled digital health technologies and patient support platforms. Contract Research Organization (CRO) is also used in clinical trials by various sponsors.
A contract research organization (CRO) is a business that supports the pharmaceutical, biotechnology, and medical device industries by offering research services that are contracted out.
Implementing artificial intelligence can shorten clinical trial cycle times while raising production costs and clinical development outcomes. Cleansing, aggregating, coding, storing, and managing the ongoing stream of clinical trial data may be made possible by AI algorithms and a robust digital infrastructure.
Every step of the clinical trial process, from finding a trial to enrollment to medication adherence, has the potential to be altered by AI-powered technology. Artificial intelligence can be used in the medical sector in various ways, from drug test identification to drug repurposing.
Not only is AI assisting in finding suitable patients, but it is also greatly enhancing data usage to track patient progress and drug success throughout clinical trials.
Let’s look at some examples of how AI is being used:
1. Clinical Trial Design
Biopharma companies are using a variety of tactics to innovate trial designs. 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 technologies can collect, organize, and analyze the growing body of data produced by clinical trials, including unsuccessful ones, and can extract valuable data patterns to aid in design.
2. Patient Enrichment, Recruitment, And Enrollment
It takes time and effort for the clinical study team and the patient to match the right patient with a fair trial. Only a few cancer patients are enrolled in clinical trials today.
AI-enabled digital transformation can improve patient selection and increase clinical trial effectiveness by mining, analyzing, and interpreting numerous data sources, including electronic health records (EHRs), medical imaging, and ‘omics’ data.
3. Patient Monitoring, Medication Adherence, And Retention
By automating data collection, digitizing common clinical assessments, and sharing data across systems, AI algorithms can aid in monitoring and managing patients.
When combined with wearable technology, AI algorithms can provide real-time insights into the safety and efficacy of treatment while predicting the risk of dropouts, improving engagement and retention.
4. Investigator And Site Selection
Choosing effective investigator sites is one of the most crucial trial components. Site characteristics affecting study timelines, data quality, and integrity include administrative practices, resource availability, and clinicians with extensive experience and knowledge of the disease.
AI technologies can assist biopharmaceutical companies in finding target sites, qualified investigators, and priority candidates. They can also gather and compile data to demonstrate to regulators that the trial process complies with GCP standards.
5. AI-Enabled Clinical Trial Analytics Powered By Operational Data
Trials generate enormous amounts of operational data. Still, functional data silos and disjointed systems can make it difficult for businesses to get a clear picture of their portfolio of clinical trials across various international sites.
Any data, regardless of its source, can be combined on a shared analytics platform supported by open data standards. This can encourage collaboration and integration and offer insights into key metrics.
Data visualization tools combined with a self-learning system intended to improve forecasts and recommendations over time can proactively provide users with accurate analytics insights.
In A Crux
In the future, all parties involved in the clinical trial process will make patient-centered decisions. Sponsors will communicate details about the trial, the procedure, and the participants through the patient.
Clinical trials can be transformed using AI-enabled patient support platforms and digital health technologies. This will increase the number of committed participants recruited, engaged, and kept in the study both during and after it is over.