# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 1 General Introduction to AI for Clinicians
### How to Read and Understand a Medical Paper with AI? [PERSON] et al. yielded a setting for evaluating diagnostic tests in clinical medicine [5]. This framework can be extended to include relevant information about AI-based algorithms. **Step 1:**_Assess whether the study outcomes are valid._  
Main guidelines  
Has there been an unconstrained, fair comparison with an explanation standard? Did the patient group incorporate an adequate gamut of patients with diagnostic tests made in clinical settings? For AI-powered methods, they can be adjusted as follows:  
Are the databases adequate and delineated in enough features? Was the reference level for training the algorithm adequate and well-founded? Subsidiary guidelines  
Did the outcomes of the assessed test affect the decision to apply the \"gold\" standard? Were the test methods described in sufficient detail to allow replication? For AI-based algorithms, they can be adapted as follows:  
Is the algorithm development methodology described in sufficient detail to allow replication? Are the algorithm/datasets used available for external validation? **Step 2:**_Evaluate the presented results._  
Are probability ratios presented for test results or is the data required for these calculations provided? Note: In statistics, the likelihood ratio test evaluates the fit of two competing statistical models. For AI-based algorithms, they can be adapted as follows:  
Are appropriate performance metrics reported? [6]  
**Step 3:**_Evaluate the usefulness of the results in the care of your patients._  
Will the reproducibility of the test result and its interpretation be satisfactory for me? Are the outcomes appropriate for sick persons? Will the outcomes replace my usual approach? Will sick persons be treated in a better way due to the new test? For AI-powered methods, they may be adjusted as follows:  
Are the conclusions of the method used responsible and explainable? Does the method show generalizability (can it be simply modified for other input data)? Was the performance of the initial method too cheerful? Has the method been confirmed in my region? Is there a cost-effectiveness evaluation for using the method? Will there be a relevant relationship with the patient's quality of life after applying the AI method? Is there any aim to calculate this act? **Example:**_Framework for evaluating a study using AI in medicine_Title of the study and associated report: _Clinically applicable deep learning for diagnosis in retinal disease_[7]. Aim: To develop a patient triage system based on AI, by means of 3D OCT data. **Step 1:**_Assess whether the study outcomes are well-founded_  
Has there been an independent, blind comparison with a reference standard? Are the datasets adequate and described in sufficient detail? Yes: The authors describe in detail the training set for OCT segmentation (Topcon) (877 scans), the validation set for segmentation (224 scans), the training set for classification (14,884 scans), the validation set for classification (993 scans), and the test set for comparing the method (997 random scans) to a reference treatment. Did the patient group incorporate an adequate range of patients for the application of the diagnostic measurements in a clinical setting? Was the benchmark for training/testing the algorithm adequate and reliable? Yes: Segmentation of the training data was manually done by trained ophthalmologists. For the purpose of classification, the validation set was annotated by three experts, and the test set used the gold standard for the complete clinical records to establish the diagnosis of the patient. The performance of the method was confronted with four ophthalmologists specializing in the retina and with four optometrists. Did the outcomes of the test in question affect the applicability of the conclusions to the reference gold standard? No, the reference gold standard was based on retrospective data from complete clinical records of patients undergoing the current standard of care.