While artificial intelligence (AI) has the potential to revolutionise the disease diagnosis process, its current value proposition remains below the expectations of most radiologists.
IDTechEx gathered the performance measurements of over 35 AI algorithms and found that most are on par with human radiologists for disease detection performance.
The goal, however, is for AI algorithms to outperform humans. Ultimately, AI must be more reliable and more accurate than even the most highly trained experts in order to gain credibility as a decision support tool.
According to IDTechEx, a number of improvements could help image recognition AI to reach its full potential as a decision support tool for radiologists.
Increasing diversity in training data sets will widen the software’s applicability
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.
While training DL algorithms, the training data should encompass numerous types of disease, lesions, and other parameters so that the algorithm can recognize a multitude of demographics, tissue types and abnormalities, and perform to the level required by radiologists.
Including more negative cases in the training data can raise algorithm specificity
During algorithm training, confirmed disease cases often take priority over negative cases to raise the algorithm’s disease detection performance.
This is beneficial for identifying patients at risk of disease but limits the AI’s ability to recognize healthy cases.
Low specificity is hence a recurring issue, which can lead to overdiagnosis and costly unnecessary procedures.
This problem can be addressed by using more curated negative cases during the training process.
Using high-resolution images will maximise algorithm performance
The use of poor-quality data during training negatively impacts the development process and performance levels of DL algorithms. Unclear images reduce the accuracy of insights generated by AI, which can damage its chances for widespread implementation.
Methods that enable doctors and radiologists to capture better images or enhance their resolution can boost the value of AI in medical settings.
AI-driven methods for assessing or improving image quality are already commercialised. Companies focused on data quality hold a competitive advantage as dealing only with high-resolution images heightens the reliability of AI-generated insights.