AI in Clinical Trials and Clinical Research
Artificial intelligence (AI) and machine learning (ML) are emerging as universal components of our daily lives. These cutting-edge technologies are already being extensively employed across a diverse array of industries, causing disruptions and paradigm shifts.
The integration of Artificial Intelligence is currently stepping into the clinical research field to enhance the efficacy of clinical trials and significantly contribute to the functioning processes of contract research organizations (CROs).
Artificial Intelligence in Clinical Trials
Drug development is a combination of various complicated and costly processes that generally take years to accomplish. Implementing AI-based technologies is gradually expanding within multiple aspects of preclinical research and clinical trials. These methodologies assist CROs, pharmaceutical, biotechnology, and medical device companies in speeding up drug development procedures, improving clinical trial efficacy, enhancing test automation, and enhancing data analysis, management, and collection accuracy.
Top-notch AI algorithms can forecast potential adverse events, better understand the toxicity of specific chemical compounds, and help minimize the possible side effects of drugs and medications.
The possibility of making predictions with AI-based models allows CROs to process and notice patterns in massive amounts of data, tailoring real-time updates and acting beyond the capabilities of humankind.
How can AI improve clinical trial design and execution?
First, through the integration of data mining, CROs can use AI to analyze vast amounts of data and understand trends, such as identifying patients who will most likely benefit from a specific treatment and finding out potential drug targets.
At NoyMed, the integration of AI-based technologies helps researchers and biostatisticians to save lots of time and accelerate the trial design processes, as AI can automatically extract data from a diversity of clinical documents, including Case Report Forms (CRFs), Adverse event Reports (AERs), Statistical Analysis Plans (SAPs), Clinical Study Reports (CSRs), etc.
AI-based technologies can monitor the behavior of patients in real-time
and identify adverse events and condition fluctuations. Due to this, our researchers working on all the globally accepted (FDA, EMA) four phases of clinical trials succeed in personalizing treatments and tailoring them to the needs of each individual patient.
Why is AI important for patient recruitment and retention?
AI algorithms assist contract research organizations in enhancing the procedures of recruiting, screening, and retaining patients.
Artificial Intelligence technologies can be extremely useful in promoting participation in clinical trials and motivating potential subjects by analyzing their behavioral patterns on social media platforms to identify users likely to be interested in a particular trial. Moreover, integrations of AI-powered chatbots can answer potential patients’ questions regarding clinical trials, boost trustworthiness, and help them to enroll. NoyMed’s AI-based software allows for conducting thorough market analysis, identifying potential patients, and offering them to take part in our clinical trials.
Regarding patient retention, activating Machine Learning (ML) models assists in predicting which patients are more likely to experience adverse events. Computer vision can thoroughly analyze medical images and identify tumors or other potential hints to assess the severity of a patient’s condition.
How does AI improve data quality and safety?
Contract research organizations can develop sophisticated Machine Learning algorithms to identify and correct errors in clinical trial data. An ML algorithm can be trained to focus on patterns that typically arise from errors made while entering data into a system, such as eCRFs, EHRs, Clinical Data Management Systems (CDMSs), and Clinical Trial Management Systems (CTMSs).
AI, especially ML algorithms developed NoyMed, generates unique encryption keys for each piece of data and uses other security measures to protect our patient’s personal information and data from unauthorized access.
How can AI optimize resource usage and cut costs?
Besides being a final point of the main benefits of CROs implementing AI and ML algorithms”, reducing costs and resource usage” is directly connected to the previous benefits that NoyMed’s clinical operations management team gets from AI.
First, implementing AI-powered technologies automates the data entry processes using optical character recognition (OCR) to extract data and scan documents. OCR significantly assists researchers in saving time and cutting costs with the automated extraction of data from medical records.
Natural Language Processing (NLP) helps CROs analyze users’ social media behavioral patterns, track if they are talking about their symptoms, and easily reach out to them to participate in different trials rather than completing this workflow manually.
Safety monitoring and early predictions of patients’ aggravation of symptoms can help researchers save resources and act quickly, creating a win-win situation.
Summary
Artificial intelligence is rapidly transforming the clinical research landscape. AI-powered technologies are being used to improve the efficacy and efficiency of clinical trials and contribute to contract research organizations’ functioning (CROs).
The use of AI in clinical trials is still in its early stages, but it has the potential to significantly improve the way that new treatments are developed and brought to market.
Overall, AI has the potential to revolutionize clinical trials by making them more efficient, accurate, and safe. As AI technology develops, we will likely see even more ways to use AI to improve the clinical trial process.