Fair shares: building and benefiting from healthcare AI with mutually beneficial structures and development partnerships

• Published in: British journal of cancer Vol. 125; no. 9; pp. 1181 - 1184
• Main Authors: Sidebottom, Richard, Lyburn, Iain, Brady, Michael, Vinnicombe, Sarah
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.10.2021; Nature Publishing Group
• Subjects: 692/699/67/1347; 692/700/1538; 706/703/270; Algorithms; Artificial Intelligence; Biomedical and Life Sciences; Biomedicine; Breast - diagnostic imaging; Cancer Research; Deep Learning; Drug Resistance; Epidemiology; Health care; Humans; Image processing; Mammography; Mammography - methods; Metadata; Molecular Medicine; Oncology; Perspective; Radiographic Image Interpretation, Computer-Assisted - methods
• DOI: 10.1038/s41416-021-01454-2
• ISSN: 0007-0920; 1532-1827

Artificial intelligence (AI) algorithms are used in an increasing range of aspects of our lives. In particular, medical applications of AI are being developed and deployed, including many in image analysis. Deep learning methods, which have recently proved successful in image classification, rely on large volumes of clinical data generated by healthcare institutions. Such data is collected from their served populations. In this opinion article, using digital mammographic screening as an example, we briefly consider the background to AI development and some issues around its deployment. We highlight the importance of high quality clinical data as fundamental to these technologies, and question how the ownership of resultant tools should be defined. Though many of the ethical issues concerning the development and use of medical AI technologies continue to be discussed, the value of the data on which they rely remains a subject that is seldom considered. This potentially controversial issue can and should be addressed in a way which is beneficial to all parties, particularly the population in general and the patients we serve.