Academics as leaders in the cancer artificial intelligence revolution

The successful translation of artificial intelligence (AI) applications into clinical cancer care practice requires guidance by academic cancer researchers and providers who are well poised to step into leadership roles. In this commentary, the authors describe the landscape of the deep learning-based AI innovation boom in cancer research. For progress in applied AI research to continue, 4 essential components must be present: algorithms, data, computational resources, and domain-specific expertise. Each of these components is available to researchers and providers in academic settings; in particular, cancer care domain-specific expertise in academia is superb. Three common pitfalls for deep learning research also are detailed along with a discussion of how the academic oncology research environment is well suited to guard against these challenges. In this rapidly developing field, there are few established standards, and oncology researchers and providers must educate themselves about emerging AI technology to avoid common pitfalls and ensure responsible use.

Automated summary

Academic cancer researchers and providers play crucial roles in guiding the successful translation of artificial intelligence applications into clinical cancer care practice. In academic settings, researchers and providers have access to key components such as algorithms, data, computational resources, and domain-specific expertise, which help drive progress in applied AI research and avoid common pitfalls.