**6. Limitations of artificial intelligence**

AI is far from being able to solve all the problems that exist in musculoskeletal disease management today. To train AI systems, large, appropriately labeled databases are needed, which are expensive to build. In addition, if there are many correlated variables, AI can establish false correlations.

*Artificial Intelligence in Musculoskeletal Conditions DOI: http://dx.doi.org/10.5772/intechopen.110696*

In radiological image interpretation, two parameters must be taken into account: accuracy and recall. An algorithm that has a high recall will classify all images with lesions as positive, but will have a low accuracy. However, an algorithm that only classifies a lesion when it is completely certain will have high accuracy but low recall. There is still great difficulty in achieving AI systems that are effective in both capabilities, so algorithms should be used depending on the clinical task to be performed: confirmation or screening [18].

On the other hand, many algorithms can only be applied in common pathologies. This makes them not applicable across the board. In addition, different AI systems may analyze the same data differently.
