demonstrate the use of machine learning to improve the accuracy of Stage II colorectal cancer prognosis. CHALLENGES AND OPPORTUNITIES Despite the widespread use of intelligent applications in healthcare there remain challenges to their adoption. The acceptance of technology-especially for diagnostics in clinical setting, concerns related to scalability, data integration and interoperability, security, privacy and ethics of aggregated digital data are just some of the examples of the challenges ahead. For example, the early adaption of AI methods in online social media analytics revealed some ethical challenges that can potentially undermine the privacy and autonomy of individuals and cause stigmatization. 11 Additionally, patient complexity is increasing with the average life expectancy in the US on the decline. 12 Baby boomers are aging (20% of the 65+ population by 2029) and multi-morbidity affects 60% of this population and is associated with over twice as many patient-physician encounters. Social and behavioral contexts play critical roles in the management of these increasingly complex patients and as such need to be key components of technology-based solutions. Despite their limitations , AI tools and techniques that are still in their infancy already provide substantial benefits in providing in-depth knowledge on individuals' health and predicting population health risks, and their use for medicine and public health is likely to increase substantially in the near future.
PubMed: MeSH publication types
Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2018). Health intelligence: how artificial intelligence transforms population and personalized health. npj Digital Medicine, 1(1), 53. https://doi.org/10.1038/s41746-018-0058-9