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MedTech Outlook | Wednesday, November 10, 2021
Artificial Intelligence (AI) can revitalize medical diagnostics. It can improve radiologists' workflow by accelerating reading times, automatically prioritizing urgent cases, and enabling early disease detection and even prevention.
FREMONT, CA: Over the next decade, AI image recognition firms supplying the medical diagnostics space will need to test and implement a slew of new features to demonstrate the technology's value to stakeholders across the healthcare system. Some of them are:
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Numerous identifications for diseases
A significant advance will be the application of AI algorithms to a variety of diseases. Currently, many AI-driven analytical tools can detect a limited number of pathologies. As a result, their usefulness in radiology practices is limited, as algorithms may miss or misinterpret disease signs for which they were not trained, resulting in misdiagnosis. Such issues may foster radiologists' mistrust of AI tools, reducing their implementation rate in medical settings. In the future, AI algorithms can recognize multiple conditions in a single image or data set.
Detecting multiple diseases from the same images involves critical radiologists providing detailed observations of every possible photographic abnormality and repeating this process thousands or even millions of times, which is time-consuming and therefore expensive. Allocating resources to develop multiple disease detection capabilities, on the other hand, will pay off in the long run for AI companies. Furthermore, software capable of detecting various pathologies, as it is more reliable and applicable, offers much greater value than software designed to see a specific pathology. Consequently, companies offering single-disease application software will soon be forced to extend their product range to remain in this competitive market.
Reduced complexity of neural networks
Today's AI models for medical image analysis have an intricate architecture, complicating the development process and increasing the amount of computing power required to run the software. Businesses developing software must ensure that their computing power is sufficient to support customer activity on their servers, necessitating the installation of costly Graphical Processing Units (GPUs). Reduced layer count while maintaining or improving algorithm performance will be a critical milestone in image recognition AI technology evolution in the future. It would require less computing power, accelerate the time necessary to generate results due to shorter processing paths, and ultimately lower server costs for AI companies.
Equipment neutrality
Installing AI software can sometimes significantly change the workflow of hospitals and radiologists. While many medical centers welcome the idea of receiving AI decision support, the reality of going through the installation process can be sufficiently daunting to deter some hospitals. As a result, software programmers put a lot of labor into making their software universally compatible to fit directly into radiologists' setups and workflows. This will become an increasingly prudent feature of customer-friendly AI recognition software compatible with all major vendors, brands, and models of imaging equipment.
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