Comparing Artificial Intelligence Platforms for Histopathologic Cancer Diagnosis
Two machine learning platforms were successfully used to provide diagnostic guidance in the differentiation between common cancer conditions in veteran populations.
Federal Practitioner. 2019 October;36(10)a:456-463
Andrew Borkowski is Chief of the Molecular Diagnostics Laboratory; Catherine Wilson is a Medical Technologist; Steven Borkowski is a Research Consultant; Brannon Thomas is Chief of the Microbiology Laboratory; Lauren Deland is a Research Coordinator; and Stephen Mastorides is Chief of the Pathology and Laboratory Medicine Service; all at James A. Haley Veterans’ Hospital in Tampa, Florida. Andrew Borkowski is a Professor; L. Brannon Thomas is an Assistant Professor; Stefanie Grewe is a Pathology Resident; and Stephen Mastorides is a Professor; all at the University of South Florida Morsani College of Medicine in Tampa. Correspondence: Andrew Borkowski (andrew.borkowski@va.gov)
Author disclosures The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.
Conclusion
We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.
Acknowledgments
The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.
