# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 1 General Introduction to AI for Clinicians
### How to Read and Understand a Medical Paper with AI?
Were the methods for performing the test described detailed enough to allow replication? Is the algorithm progress explained in enough detail to permit its replica? Are the algorithm and datasets used obtainable for external confirmation? The researchers explained the application of the U-net structure in detail but noted that the data are not publicly accessible but may be disposable upon solicitation, conditional on local and national ethics approval. Yet, in a later paper, those researchers show the availability of the segmentation algorithm and dataset publicly available for validation [8]. **Step 2:**_Assess the reported outcomes_  
Are the probability ratios of the outcomes given, or at least the data needed for their computation provided? Are performance metrics adequately reported? [6]  
Yes: The authors report receiver operating characteristic (ROC) curves (see Appendix B), confusion matrices, accuracies, and the influence of the extra data (OCT only, OCT + fundus + entire case abstract) on experts' conclusions. The method benefited from an AUC (area under the ROC curve, Appendix B) of 99.21% and an error rate of 5.5% (55/997). **Step 3:**_Assess the usefulness of the outcomes for patient care_  
Will the test's reproducibility and its explanation be adequate for the attending physician? Are the outcomes of the method used explainable? Does the method show generalization? Was the performance of the original method too cheerful? The authors described information for generalizing the use of their method by means of other OCT equipment (Spectralis). Although initially, the method used performed badly and had an error rate of 46.6% for classification, training the segmentation method once again increased the AUC to 99.93% and decreased the error rate to 3.4% (4/116). Thus, the software proved to be adjustable to another machine. Algorithm developers also tried to incorporate explainable elements into the AI area by yielding segmentation maps highlighting retinal composition, pathology, artifacts, and predictions of diagnostic likelihood and recommendations. Yet, in the videos used as additional documents, the automatic segmentation was not always precise. * Are the outcomes pertinent to the sick persons? Equivalently, are we in the case of _personalized medicine_ (a very trendy approach now)? * Has the algorithm been validated in the local population? No, the outcomes are from patients at Moorfields Hospital, London, UK. They will need further confirmation in different ethnicities and research centers before they can be applicable to your patients. * Will the results change my management? * Is there any contrast of the new method with current standards of healthcare? * Is there any cost-effectiveness examination to justify using the algorithm? The algorithm was analyzed by four ophthalmologists and four optometrists. It performed in a similar way or even performed better than the specialists. However, there was no endeavor to evaluate its cost-effectiveness against industry standards of care. * Will patients be better cared for because of the test? * Will there be a relevant impact on the patient's health after implementing the new method? * Is there any endeavor to estimate or calculate its impact? The algorithm has the capacity to act as a decision aid for the clinician, but the immediate impact on the patient cannot be evaluated. No trial was carried out by the stakeholders to compute possible real-world impact.