*4.1. Uses of AI in the Diagnosis of Diabetes*

Under this area of interest, the papers published in this category center around the diagnosis of diabetes using branches of AI. The intention of such research is not to replace the diagnosis work done by the doctors in healthcare, but to complement their efforts as such to optimize the patient's diagnosis timings as well as to cut down on the workload burden.

There are many methods published under this category, examples include the diagnosis of type II diabetes via the analysis of parameters such as heart rate variability and arterial blood glucose alterations. They are performed using non-linear methods such as detrended fluctuation analysis (DFA) and Poincare plot to produce two metrics termed standard deviation ratio (SDR) and alpha-ratio. These two metrics are then fed into a machine-learning algorithm to dichotomize the subjects as diabetic or non-diabetic. The paper reports an accuracy of 94.7% in the correct categorization of subjects, which offers the possibility that it could be further developed as a non-invasive screening tool for predicting whether an individual has type II diabetes [15].

Using AI to diagnose different conditions such as diabetes is interesting, but it comes with drawbacks as well. The main problem with AI is that firstly, it needs to be well-trained, which would require a large number of patients in the training set. This is a problem because personal details protection and privacy is a concern. Another problem with AI diagnostics is that it needs to be consistent, replicable, and reliable, but so far many of the studies have not been applying the evidence-based approaches that are seen in established fields [16].

There have been questions about whether the use of AI can improve diagnostic ability in terms of efficacy and time reduction. For instance, a study found the AI model can detect breast cancer in whole slide images better than 11 pathologists, with the pathologists being limited by the allowed assessment time of one minute per image, while the AI is not limited by any factors. However, when the pathologists were given unlimited time, they performed similarly to the AI and detected more difficult cases more frequently than the computers [17]. This raised the possibility that AI may better be employed in the diagnosis of clear-cut simple cases, whereas more complex cases that require detailed assessment may be more worthwhile to be tackled by humans.
