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Purpose: Despite tremendous gains from deep learning and the promise of AI in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards like the TRIPOD, CONSORT, and CLAIM checklists is increasing to improve the peer review process and reporting of AI tools. However, no such standards exist for product level review. Methods: A review of the clinical trials shows a paucity of evidence for radiology AI products; thus, we developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. We applied the assessment tool to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools. Results: We find that there is limited technical information on methodologies for FDA approved algorithms compared to open source products, likely due to concerns of intellectual property. Furthermore, we find that FDA approved products use much smaller datasets compared to open-source AI tools, as the terms of use of public datasets are limited to academic and non-commercial entities which preclude their use in commercial products. Conclusion: Overall, we observe a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring the actual performance of AI tools in clinical practice.