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There is a new future for healthcare on the horizon. In this world, artificial organs will utilize a number of sensors to optimize the body’s chemical balance. Vaccines will exist for diseases that were thought to be incurable not too long ago. In fact, by 2030, the first CRISPR babies will enter their teenage years. The healthcare ecosystem is always evolving, but the level of change we face in the next decade is unprecedented.
In the past, incumbents could rely on the fact that new entrants faced high barriers to entry: the extreme complexity of managing the cost of care and the highly regulated, capital-intensive, and very low-margin nature of the sector.
Alongside mainstays such as BI/AI, the topic garnering most interest in the pre-symposium questionnaire was patient engagement. Today, about a quarter of respondents across all global regions say it is a focus of investment moving forward.
There is no doubt that radiology could benefit from AI, radiomics, and big data. The size and complexity of image datasets are increasing to the point that existing IT systems and radiologists can barely cope.There’s also too much emphasis on time-consuming image manipulation and tedious tasks, resulting in quality variability and fatigue.
A new machine learning system created by UCLA researchers may help doctors classify breast cancers that are notoriously difficult to diagnose, according to an Aug. 9 study published in JAMA Network Open.