Key concerns when evaluating AI in radiology diagnostics
As part of their work, radiologists may need to evaluate the merits of an AI solution or the soundness of the AI algorithms being deployed. To assess the efficacy of AI algorithms for use in the interpretation of medical images, a methodological framework in which important AI parameters are evaluated can be of immense value.
For instance, to understand an algorithm’s level of sensitivity and specificity in, say, differentiating lung cancer from benign diseases when examining lung nodules, a so-called receiver operating characteristic curve analysis can help determine the discrimination performance of the algorithm under consideration. Also of importance is calibration performance, which evaluates how similar predicted probability values will be to actual probabilities. The use of data collected with a minimum of spectrum bias is likewise of critical importance: Subjects forming the training data—both with and without disease—must be representative of the patients in whom the algorithm will be used, including severity or duration of the disease, presence and severity of comorbidities, and demographic characteristics. Otherwise, the results risk being biased and ultimately become clinically unusable.
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Furthermore, unnatural disease prevalence within the data used to develop an algorithm could lead to problems. Determining the probability of lung cancer, using the example earlier, will be inaccurate in a population in which disease prevalence differs from that in the dataset used to develop the algorithm. Finally, beyond performance metrics, the best demonstration of clinical efficacy from a diagnostic AI tool is improved patient outcomes, which can be identified in clinical trials and observational outcome research.
features regulatory-approved diagnostic AI that analyzes medical images. The developer, product name, application, and image source for each is provided.
Various initiatives exist today for third-party providers to work with the AI development teams at some of the world’s biggest companies, producing AI-enabled solutions specific to healthcare that could impact the work of medical professionals, including radiologists.
, the chipmaking giant works closely with clients on integrating their AI mechanisms with Intel’s AI systems into a seamless and unified workflow that radiologists can then use. Intel sees its main role throughout the product development continuum as one of making algorithms work faster and of optimizing the pipeline. “Faster AI algorithms can translate into speedier results and seamless clinical workflows, a critical factor in clinician adoption. By optimizing these compute-intensive applications with tools like OpenVINO, clinicians don’t have to wait for AI, the AI becomes an invisible aid,” says Dr. Anthony Reina, chief AI architect for health and life sciences at Intel, and a medical doctor with extensive experience in neurophysiology, telemedicine, and data science.