The report claims that image recognition AI has the potential to revolutionise medical diagnostics. In addition to enabling early disease detection and even the possibility of prevention, it can enhance the workflow of radiologists by accelerating reading time and automatically prioritizing urgent cases.
The report with the title “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts” said that to enable efficient analysis of patient scans, image recognition AI software should be able to combine and interpret data from different imaging sources to gain a better perspective of the patient’s pathology. This could generate deeper insights into disease severity and progression, thereby providing radiologists with a higher level of understanding of the condition of patients.
Some AI companies are already attempting to train their algorithms using data gathered from different imaging methods into one comprehensive analysis, but this remains a challenge for most. Recognising signs of disease in images from multiple modalities requires a level of training far beyond the already colossal training process for single modality image recognition AI. From a business perspective, it is currently simply not worth it for radiology AI companies to explore this due to the sheer quantity of data sets, time and manpower required to achieve this. This suggests that sensor fusion will remain an issue for the rest of the coming decade.
The report suggests another important development will be to apply image recognition AI algorithms to multiple diseases. Currently, many AI-driven analysis tools can only detect a restricted range of pathologies. Their value in radiology practices is limited as the algorithms may overlook or misconstrue signs of disease that they are not trained on, which could lead to misdiagnosis. This could lead to a mistrust of AI tools by radiologists, which may in turn reduce their rate of implementation in medical settings.
In the future, AI algorithms will recognise not just one but various conditions from a single image or data set. This is already a reality for numerous radiology AI companies. For example, DeepMind’s and Pr3vent’s solutions are designed to detect over 50 ocular diseases from a single retinal image, while VUNO’s algorithms can detect a total of 12.
Detecting multiple pathologies from the same images requires expert radiologists to provide detailed annotations of each possible abnormality in a photo, and to repeat this process thousands or even millions of times, which is highly time-consuming and thus expensive. As a result, some companies prefer to focus on a single disease. Allocating the resources to achieving multiple disease detection capabilities will be worth it on the long run for AI companies, however. Software capable of detecting multiple pathologies offer much greater value than those built to detect a specific pathology as they are more reliable and have wider applicability. Companies offering single-disease application software will soon be forced to extend their product’s application range to stay afloat in this competitive market.
A key technical and business advantage lies in the demonstration of success in dealing with a wide range of patient demographics as it widens the software’s applicability. AI software must work equally well for males and females, different ethnicities, the report said.
The report said that the architecture of AI models used in medical image analysis today tends to be convoluted, which extends the development process and increases the computing power required to use the software. Companies developing the software must ensure that their computing power is sufficient to support customers’ activities on their servers, which requires the installation of expensive Graphical Processing Units (GPUs). In the future, reducing the number of layers while maintaining or improving algorithm performance will represent a key milestone in the evolution of image recognition AI technology. It would decrease the computing power required, accelerate the results generation time due to shorter processing pathways and ultimately reduce server costs for AI companies.
The installation of AI software for medical image analysis can sometimes represent a significant change to hospitals’ and radiologists’ workflow. Although many medical centres welcome the idea of receiving decision support through AI, the reality of going through the installation process can be daunting enough to deter certain hospitals.
As a result, software providers put a lot of effort into making their software universally compatible so that it fits directly into radiologists’ setups and workflows. This will become an increasingly desirable feature of image recognition AI as customers favour software that is compatible with all major vendors, brands and models of imaging equipment. This is, broadly speaking, already a reality as most FDA-cleared algorithms are vendor-neutral, meaning that they can be applied to most types of scanner brands and models.
The idea of integrating image recognition AI software directly into imaging equipment is gaining momentum as it would facilitate the automation of medical image analysis. In addition, it avoids problems with connectivity as no cloud access is required. This is being done more and more – recent examples include Lunit’s INSIGHT CXR software integration into GE Healthcare’s Thoracic Care Suite and MaxQ AI’s Intracranial Haemorrhage (ICH) technology being embedded into Philips’ Computed Tomography Systems.
A downside of integrating AI software into imaging equipment is that the hospital/radiologist has no flexibility to choose the provider/software that best suits their needs. The value of this approach depends on the performance level and capabilities of the integrated AI software, and if it matches the user’s requirements. If that is not the case, hospitals are likely to favor cloud-based software, the report said.
From the equipment manufacturer’s point of view, the business advantage of integrating image recognition AI into their machines is clear. The enhanced analytical capabilities provided by the AI software would give OEM manufacturers a competitive edge as they render the machines more appealing to hospitals seeking to boost revenues by maximising the number of patients seen every day.
From a software provider’s perspective, the situation is less clear. AI radiology companies are currently considering the advantages of entering exclusive partnerships with manufacturers versus making their software available as a cloud-based service. IDTechEx expects a divide to arise among AI radiology companies in the next five to 10 years. Some will choose the safe option of selling their software exclusively to large imaging equipment vendors due to the security that long-term contracts can provide. Others will lean more towards continuing with the current business model of catering directly to radiology practices.