As artificial intelligence (AI) continues to make inroads in the medical field, its application in radiology has become crucial for improving patient care and enhancing diagnostic accuracy. However, the issues of transparency and reproducibility in AI research have long plagued the development of the field. Recently, a study published in "Nature Communications" demonstrated how cloud-based infrastructure can implement and share transparent and reproducible AI pipelines in radiology, providing new momentum for clinical translation.
The study, conducted by Dennis Bontempi, Leonard Nürnberg, Suraj Pai, and several other experts, utilized the computing and data hosting capabilities of cloud platforms to achieve end-to-end reproducibility, from data retrieval to deep learning inference, and from post-processing to the analysis of final results. The research team successfully replicated two AI-based cancer imaging biomarker studies and extended validation on previously unseen data.
The cloud infrastructure used in the study not only improved the efficiency of data processing but also supported the reproducibility of AI research by providing a consistent computing environment, simplifying data exploration and access, and facilitating the storage and sharing of code and results. Additionally, the study provided easy-to-extend pipeline examples for the broader oncology field, promoting the application of AI in radiology.
The lead author of the study, Dennis Bontempi, said, "The use of cloud resources not only enhances the reproducibility of research but also accelerates the translation of AI algorithms into clinical solutions. Our workflow provides a practical method for researchers and practitioners to operate AI pipelines while promoting best practices and open software in AI for radiology."
Although cloud infrastructure has great potential to promote the reproducibility of AI research, the research team also pointed out the challenges of adopting cloud resources, including the learning curve, data privacy and security issues, and dependence on third-party service providers. However, the authors believe that with the continuous development and maturation of cloud technology, these challenges will gradually be overcome, and cloud technology will become an important driving force in medical AI research.
