Clinical Trial Design and Artificial Intelligence | Pepgra.com

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Clinical trials take up the second half of the ten – 15 year, 1.5 – 2.0 billion USD, cycle of development only for introducing a replacement drug within a market. Therefore, any Clinical Trials Services that end in failure tends to translate not just in losses in terms of investment for the clinical trial but also takes into its ambit the prices incurred as preclinical development costs. 

 This renders the loss for each failed clinical test at 800 million to 1.5 billion USD(Harrer et al., 2019). Keeping these factors in mind, it might be beneficial to explore how AI or AI because it is popularly known are often effectively utilized to re-mould the key phases of a Clinical Trail design with a view to reinforce the speed of success within the trial.


Factors causing Failures in Clinical Trials and how AI can help

The main two factors that cause failure within clinical trials services pertains to the choice of patient cohorts and therefore the mechanisms used for recruiting cohorts. The said two factors aren't enough to usher in the foremost appropriate patients to the trial during a timely manner, along side the absence of a technical infrastructure to match the intricacies of executing a clinical trial(Fogel, 2018).

AI and its Evolution

AI within the domain of drugs features a history which matches back to the first 1970s when proficient systems like MYCIN had been initially introduced for providing support to diagnostic decisions(Shortliffe, 1984). Nonetheless, early systems of medical AI depended largely on experts from the medical domain for training computers by encrypting clinical knowledge as logic based rules for particular situations or scenarios. Systems of such kind were wrought with the limitation wherein it had been intensive to labour and required tons of your time for the development process. Also, once it had been developed there have been also considerable challenges with regards to any future updates intrinsically systems were largely rigid(McCauley & Ala, 1992).


AI in Clinical Trials

he figure above envisions the key techniques through which AI are often introduced within the tenets of clinical design. There are three key themes within the planning which comprises of; cohort composition, patient recruitment and patient monitoring. These are founded on the features of the patient in terms of eligibility, suitability, empowerment, enrollment and motivation. this is often also inclusive of aspects concerning trial such as; adherence control, endpoint detection and retention of patients. An array of methodologies for design has been utilized to execute functionalities of the target.

To Continue Reading blog: https://www.pepgra.com/blog/clinical-trial-design-and-artificial-intelligence/


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