Medical Diagnostics Improved By Using AI Technological Innovations

Image recognition Artificial Intelligence (AI), has the potential for an AI technological medical revolution in medical diagnostics. It is used to detect early diseases or maybe prevent them. Additionally, it can improve the workflow of radiologists by speeding up reading times and prioritizing urgent cases.

As IDTechEx reported in their article ‘AI in Medical Diagnostics – Current Status and Opportunities for Improvement,’ the current value proposition of image recognition AI is still below that of most radiologists. AI image recognition companies that serve the medical diagnostics industry will need to implement many features over the next decade to improve the value of their technology for stakeholders in the healthcare sector.

Interpret Data By Combining Sources Via Sensor Fusion

Radiologists can use a variety of imaging techniques to detect disease signs. For example, X-ray and CT scanning are both used to detect respiratory diseases. While X-rays are more cost-effective and faster, CT scanning is quicker and provides more information about lesion pathology because it can create 3D images of your chest. Sometimes, it is necessary to follow up a chest CT scan with an X-ray. However, AI-driven analysis software cannot process both.

Image recognition AI software must be able to combine data from multiple imaging sources to provide a better understanding of patient pathology. This allows radiologists to gain deeper insight into the disease progression and severity, leading to a better understanding of the patient’s condition.

While some AI companies have already begun to use data from multiple imaging methods to train their algorithms, this is still a significant challenge for many. Recognizing signs of disease in images taken from different modalities is a complex task that requires additional training beyond what is needed for image recognition AI. This is a costly business venture that radiology AI companies cannot afford to pursue. It’s simply too expensive in terms of the data sets and time required. This means that sensor fusion will continue to be a problem for the next decade.

Various Diseases Applications

Another innovation is to use image recognition AI algorithms for several diseases. Many AI-driven pathology analysis tools are limited in their ability to detect specific pathologies. The algorithms can miss or misunderstand signs of illness, which could result in misdiagnosis. Their utility in radiology practice is therefore limited. These issues can lead to mistrust in radiologists’ AI tools, reducing their effectiveness in medical settings.

AI algorithms will recognize multiple conditions from one image or data set in the future. For example, numerous retinal diseases are identified from a single fundus photo. Numerous radiology AI technological companies already recognize this as a reality. DeepMind and Pr3vent, for example, can detect more than 50 ocular diseases using a single retinal picture, while VUNO can detect 12.

Expert radiologists are required to identify multiple pathologies in the same image. They must also provide detailed annotations for each abnormality. This process can be repeated thousands of times or more, which can be costly and time-consuming. Some companies choose to concentrate on one disease. AI companies find it worthwhile to allocate resources to multiple disease detection capabilities in the long term. Software that can detect various pathologies is much more valuable than software designed to detect one pathology. It is reliable and has a wider range of applicability. Software that detects only one disease will be out of business soon.

All-Encompassing Training Data

The ability to successfully deal with a broad range of patient demographics is a key technical and business advantage. AI software should be equally applicable to males and women, as well as different ethnicities.

Deep learning algorithms are trained to recognize a particular disease. The training data should include many types of abnormalities that are associated with the disease. The algorithm will be able to recognize signs of the disease in many different demographics and tissue types that radiologists require. Breast cancer detection algorithms, for example, must recognize lesions of all types (e.g., different densities). Skin cancer is another example.

As moles appear differently in different skin tones, the skin cancer detection algorithms have had difficulty in the past identifying suspicious moles. These algorithms are able to examine moles of all skin types and colors. The software must be able to recognize the stage of disease progression from an image of a suspicious mole based on its shape and color. If an algorithm sees a type of abnormality it doesn’t recognize, it will not associate it with any other condition it knows. A diverse data set helps to avoid bias (an algorithm’s tendency to ignore options that are contrary to its initial assessment).

Reduced Neural Network Complexity

Today’s AI models are often complex, making them difficult to develop and requiring more computing power. Software developers must ensure that their servers have sufficient computing power to support their customers’ activities. This requires expensive Graphical Processing Units (GPUs). Future milestones in image recognition AI technology will include reducing layers and improving algorithm performance. This would reduce the amount of computing power needed, speed up the results generation process, and ultimately lower server costs for AI companies.

Imaging Equipment Universally Compatible

Sometimes, the installation of AI software to analyze medical images can cause significant disruptions in radiologists’ and hospitals’ workflows. While many medical centers welcome the idea of AI decision support, some hospitals may find the process daunting.

Software providers have spent a lot of time making their software compatible with radiologists’ workflows and setups. Image recognition AI customers will increasingly value software that is compatible across all brands and types of imaging equipment. This is already possible because most FDA-cleared algorithms are used with all types of scanner models and brands.

Limited Patient Information And The Potential Treatment Plan

AI algorithms have limited access to medical imaging data. During the analysis process, AI software cannot access the medical history and condition of patients. This limitation means that the software can only detect abnormalities and provide quantitative information. In some cases, it can also assess the risk of developing the disease.

These insights are valuable for doctors and can be used to improve patient care. However, AI can do much more than doctors can. Software developers need to focus on post-diagnosis support in order to fully exploit AI’s capabilities and add value in medical settings. This is a relatively new area of AI medical image recognition, but companies are exploring this possibility.

Skin cancer detection apps like MetaOptima or SkinVision offer actionable recommendations that can be taken after the assessment has been completed. You can schedule follow-up appointments or schedule a biopsy, or set reminders for your next skin check. As it is almost like a second opinion and gives the doctor more confidence, post-diagnosis support has become a popular feature.

Doctors ultimately seek a solution that allows them to develop viable treatment plans. The software must have information about patients’ electronic health records, clinical trials results, drug databases, and other pertinent information. This is more than simple image recognition. Currently, most companies do not have any plans for this. Because of the interoperability and overlap between different databases and hospitals, implementation of these systems will continue to be a work in process for the next ten years.

AI Technological Software Build Into Imaging Equipment

Image recognition AI software can be integrated directly into imaging equipment, such as MRI scanners or CT scanners. It would allow for automated medical image analysis, and it is gaining momentum. It also avoids connectivity problems as it does not require cloud access.

This is becoming more common. Recent examples include Lunit’s INSIGHT software integration into GE Healthcare’s Thoracic Care Suite and MaxQ AI’s Intracranial Hemorrhage technology embedded into Philips’ Computed Tomography System.

The downside to integrating AI software in imaging equipment is that hospitals/radiologists are not able to choose the provider/software best suited for their needs. This approach is dependent on its performance and compatibility with the requirements of the user. Hospitals will likely prefer cloud-based software if this is not the case.

Equipment manufacturers see the clear business benefit of integrating image recognition software into their machines. OEM manufacturers would have a competitive advantage because of the AI software’s enhanced analytical capabilities. This makes the machines more attractive to hospitals looking to increase revenues and maximize the number of patients they see every day.

The situation is less clear from a software provider’s point of view. AI radiology companies are considering whether it is better to form exclusive partnerships with manufacturers than to make their software available via a cloud-based service.

Experts predict that there will be a division among AI technological radiology firms over the next five to ten years. Because of the security long-term contracts offer, some will opt to sell their software only to large imaging equipment vendors. Others will prefer to continue with the existing business model of catering directly to radiology practices.

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