How AI is Improving Clinical Trials

AI is a versatile tool that can be applied in every stage of drug development to improve the efficiency of clinical conduct and pharmacovigilance (PV).

FREMONT, CA: AI's ultimate objective is to replicate the human decision-making process using machine simulations of human cognitive functions like learning, reasoning, and self-correction. AI is quickly establishing itself as an all-powerful solution to a wide range of healthcare management issues.

Machine learning (ML), deep learning (DL), natural language processing (NLP), and optical character recognition (OCR) are examples of AI techniques. The extensively used machine learning (ML) technique in the pharmaceutical industry helps to build data analytical algorithms and mathematical models to extract features from sample data to make predictions or choices.

Unsupervised learning, used for data extraction, and supervised learning, applied for predictive modeling, are two types of machine learning. DL is a subset of machine learning approaches related to artificial neural networks that employ numerous hidden layers to collect and process complex data from raw data. Another aspect of drug development is natural language processing (NLP), which derives meaning from textual or natural language data. The goal of OCR is to electronically transform images of typed, handwritten, or printed text into machine-encoded text using pattern recognition and computational vision.

Early disease prognostication, diagnosis, and treatment, outcome prediction, and prognosis evaluation, personalized treatments, behavior modification, drug discovery, manufacturing, clinical trial research, radiology and radiotherapy, smart electronic health records, and epidemic outbreak prediction are all areas where machine learning-based applications are used. 

Even though AI could have been helpful in the COVID-19 pandemic for monitoring and prediction, diagnosis and prognosis, treatments and vaccines, and social control, its effectiveness was restricted by a lack of information, too much data, and data privacy concerns.

For the pharmaceutical business, AI is proven to be a versatile tool that can be used at various stages of drug discovery, including drug target identification and validation, developing new molecules, reusing old medicines, enhancing clinical trial efficiency, and pharmacovigilance (PV). Clinical drug research, which is hampered by high expenses and failure rates, is one area where AI is being explored.

DL has had a lot of success discovering promising novel medication candidates and improving predictions of their qualities and potential dangers. AI can speed up the search for correlations between indications and biomarkers and assist in selecting lead compounds with a better probability of success throughout clinical development.

AI can help in improving patient selection by:

Reducing population heterogeneity- This could be accomplished by harmonizing massive EMR data from various forms and levels of accuracy and using electronic phenotyping.

By prognostic enrichment – Patients with a better chance of having a detectable clinical objective are chosen. Machine learning approaches based on essential biomarkers of Alzheimer's disease (AD) are used for prognostic enrichment.

By predictive enrichment – Selecting a community that is more likely to react to a treatment.

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