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Pharma Tech Outlook | Monday, May 22, 2023
Pharma AI has many benefits for drug development, such as enhancing the success rate of clinical trials, Virtual collaboration, and improving treatment outcomes, which increases revenue and productivity.
FREMONT, CA: Using Artificial intelligence(AI) in the pharmaceutical industry can drive revenue growth and operational efficiency by providing in-depth data mining and analytics, patient engagement, compliance monitoring, and making efforts. R&D time and costs can also be affected.
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New drugs can benefit from AI in the following ways:
Integrated Information System and Automated Big Data Mining Speed Up Drug R&D: Rapid drug research and development is one of the most significant benefits of AI in the pharmaceutical industry.
Machine learning and deep learning algorithms are used to extract and analyze raw genotypic and phenotypic data.
It streamlines and speeds up the collection of big data from Internet of Things (IoT) devices, medical research journals, and other public and private sources.
It is possible to reduce medicine discovery time with AI systems that collect R&D data automatically and relatively fast, which is long with human-centric approaches to medical discovery. Faster delivery of new drugs to hospitals and patients can give pharmaceutical companies a competitive edge and increase turnover.
Monitoring medication adherence: The drug R&D process can be slowed by low medication compliance1 rates among patients participating in clinical trials. A candidate's inability to adhere to a prescribed drug can also undermine the testing and validation process.
Non-adherence is not solved by traditional health IT systems and human-centric approaches, such as requiring patients to memorize their dosage. There are several ways in which AI can be used to track pharma compliance rates. Using indigestible IoT sensors, pharma researchers can observe and examine drug usage against treatment results and side effects.
Blood pressure and glucose levels can be tracked using the technology. By using machine learning, it can identify anomalous outcomes.
Facial recognition software is another option. After a patient records themselves taking a drug, an AI algorithm analyzes the video to ensure that the right candidate took it.
Streamlining the pharmaceutical sales process: Using AI-powered pharma sales software can significantly impact rep productivity and turnover. It is possible, for example, to study industry trends and customer preferences through technology, such as which treatment options are preferred by a particular practice or doctor.
This intelligence can be used in pre-calling planning to gather relevant promotional and informational materials. An effective pre-meeting preparation increases the likelihood of converting leads into sales. Pharma revenue can be boosted by it.
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