With success in both in vitro and in vivo studies, scientist can propose that clinical trials test whether the product be made available for humans.
[2][4][5] Also, in recent years many candidate drugs failed in phase 3 trials because of lack of efficacy rather than for safety reasons.
As such, a product that fails during clinical trials may simply be abandoned, even if a small modification would solve the problem.
This stifles innovation, decreasing the number of truly original biomedical products presented to the market every year, and at the same time increasing the cost of development.
[14][15] The development has accelerated in recent years following the growth of computer capacity and more advanced simulation models, and is now at the point that virtual platforms are gaining acceptance by regulatory bodies as a complement to conventional clinical trials for new product introductions.
[16] A complete framework for in-silico clinical trials in radiology needs to include the following three components: 1) A realistic patient population, which is computer simulated using software phantoms; 2) The simulated response of the imaging system; 3) Image evaluation in a systematic way by human or model observers.
[20] In addition to the above, models have been developed for organ and patient motion, blood flow and contrast agent perfusion.
[23] Image interpretation based on deep learning and artificial intelligence (AI) is an active research field,[24] and might become a valuable aid for the radiologist to find abnormalities or to make decisions.