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AI and machine learning can be beneficial at all stages of the drug development process.
Fremont, CA: When it comes to digital health technology, the pharmaceutical sector has been sluggish to accept it, and pharma businesses, in general, have taken a long time to deploy AI and machine learning methods, making broad-scale digital transformation challenging.
There is a significant possibility for medication discovery and development, but it depends on enterprises' ability to incorporate new health technology into everyday strategies.
While the healthcare business is quickly adopting digital technology, the pharmaceutical industry is trailing in digital maturity. Any actions taken to catch up by early movers are patchy owing to a lack of strategy & digital-focused leadership.
AI and Machine Learning in Drug Development
Medicine development takes enormous time and money – according to Taconic Biosciences, bringing a drug to market costs roughly $2.8 billion over a 12-year period.
AI and machine learning can be beneficial at all stages of the drug development process. Healthcare AI companies raised more than $2 billion in Q3 2020, with those utilizing AI to expedite the medication manufacturing process receiving some of the largest sums compared to businesses applying the technology in other healthcare industries.
• AI in Drug Discovery
The drug development process includes everything from reading and interpreting existing literature to examining how prospective medications interact with targets. According to Insider Intelligence's AI in Drug Discovery and Development study, AI might save corporations up to 70 percent on drug discovery expenditures.
• AI in Preclinical Development
During the preclinical research stage of drug discovery, promising therapeutic targets get tested using animal models. Using AI during this phase might help trials go more smoothly and allow researchers to forecast how medicine will interact with the animal model more promptly and successfully.
• AI in Clinical Trials
After passing the preclinical development phase and gaining FDA permission, researchers begin testing the medication on human subjects. Overall, this four-step procedure gets widely regarded as the most time-consuming and expensive stage of the drug-making process.
AI can help with participant monitoring throughout clinical trials by collecting a wider collection of data more rapidly and by tailoring the trial experience.