How Does AI Change Clinic Trials?

Many clinical trials use artificial intelligence (AI) to enhance operational effectiveness, hasten medication discovery, and cut costs. It may be used to access data from various sources, including scholarly journals, illness registries, and medical records. It can assist in more efficient and effective patient recruitment.

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Clinical trial advances crucial to the transformation of clinical trials are made feasible by AI technologies, including the seamless integration of phases I and II of clinical trials, the creation of unique patient-centered outcomes, and the collection and analysis of Real-World Data.

The system can even eliminate the requirement for animal models and placebo-controlled arms by predicting which individuals would benefit from therapy and which ones may be in danger.

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How Can AI Transform Clinical Trials?

Clinical trials might change as a result of the use of AI in several major areas:

• Clinical Trial Design

To develop the trial design, biopharmaceutical companies are implementing various tactics. Trial design has been stimulated 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 gather, organize, and analyze the growing quantity of data produced by clinical trials, including unsuccessful ones. They can uncover useful patterns of data to aid in design.

• Patient Recruitment, Enrichment, and Enrollment

It takes time and effort for the clinical research team and the patient to match the appropriate patient with the correct trial. Only 3% of cancer patients are really recruited in clinical trials nowadays.

Through the mining, analyzing, and interpreting of several data sources, like electronic health records, medical imaging, and "omics" data, AI-enabled digital transformation can enhance patient selection and boost the success of clinical trials.

• Patient Monitoring, Medication Adherence and Retention

By automating data collection, digitizing common clinical evaluations, and transferring data between systems, AI algorithms can assist in monitoring and managing patients.

Combining wearable technology with AI algorithms can improve engagement and retention by enabling continuous patient monitoring, real-time insights into the security and efficacy of therapy, and the prediction of dropout risk.

• Driving AI-Enabled Clinical Trial Analytics with Operational Data

Trials generate enormous amounts of operational data, but functional data silos and disjointed systems can make it difficult for businesses to clearly understand their portfolio of clinical trials across many international sites.

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