With artificial intelligence (AI) precipitously perched at the apex of the hype curve, the promise of transforming the disparate fields of healthcare, finance, journalism, and security and law enforcement, among others, is enormous. For healthcare – particularly radiology – AI is anticipated to facilitate improved diagnostics, workflow, and therapeutic planning and monitoring. And, while it is also causing some trepidation among radiologists regarding its uncertain impact on the demand and training of our current and future workforce, most of us welcome the potential to harness AI for transformative improvements in our ability to diagnose disease more accurately and earlier in the populations we serve. Highlights As in the case of most disruptive technologies, assessment of and consensus on the possible ethical pitfalls lag. selection bias in AI datasets can result in inaccurate results in under-represented patient groups. AI models with imaging data acquired from one setting may poorly generalize to other practice settings. Patient image data ownership regulations vary by country and domain. De-identification of image data used in AI algorithms may inadvertently reveal protected health information. Authors: Nabile M. Safdar, John D. Banja, Carolyn C. Meltzer Article:https://doi.org/10.1016/j.ejrad.2019.108768

Example of a ‘clinical intelligence system’

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care.

This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated ‘digital clinics’. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased ‘digital literacy’ for all those implicated in their care.

For complete article:

Kooman JP, Wieringa FP, Han M, et al. Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?. Nephrol Dial Transplant. 2020;35(Suppl 2):ii43-ii50. doi:10.1093/ndt/gfaa015