MEDICAL DEVICES
y providing financial fleibility, s can
alleviate the brden on small biotechs and ensre smoother progress throghot the trial lifecycle. This is especially important dring earlyphase dosefinding stdies, where worloads can change drastically from one month to the net. onthly payments shold reflect the wor performed and not be based on otofthebo algorithms that are sed for laterphase trials. The srge in AI applications represents a
facet of the ecitement srronding this transformative technology. There has been sbstantial capital infl into AI healthcare initiatives. Althogh investment declined from its pea, still saw $. billion invested across 5 deals. espite maret challenges, highality projects contine secring sbstantial fnding in , nderscoring the resilience and attractiveness of the AI healthcare sector. owever, these favorable trends do not
garantee companies sccess in achieving sstainable bsiness models. ltimately,
46 | Outsourcing In Clinical Trials
adoption is a ey parameter for evalating sccess after commercial lanch. A great prodctmaret fit is jst the beginning of the healthcare related prodct lanch. eglatory, patient safety, worflow, cost, infrastrctre, hman resorces, and other barriers can prevent hospital implementation of new technologies. nsrprisingly, healthcare adoption of most of the new technologies lags other indstries by 5 years or more. sing AI in radiology, representing most AI
healthcare projects, as an eample only of radiologists se AI clinically, and of practices plan to invest in AI tools in the net five years, according to a poll condcted by the American ollege of adiologys ata cience Institte. To increase awareness and overcome AI implementation barriers, pioneers now share eperiences regarding technical validation, I analysis, data ality, the blac bo enigma, infrastrctre and technical compleities, worflow integration, and ethical implications. owever, even addressing these, withot etra payment and coverage policy for
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