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The future of Health Care is tied to AI and big data

The data in healthcare keep growing. The real issue is how to make use of them. Credit: Stanford – link in the post

The future of Health care, broadly speaking, is progressing along two directions: better understanding of the single individual and leveraging data from a multitude of individuals.

Both are fuelled by increasing capability of “seeing” what is going on -that includes the genome sequencing-, thanks to better sensors and harvesting capabilities and increasing capability to make sense of what is going on. Big Data and Artificial Intelligence are at the core of these evolutions.

The human body is a tremendous source of data. The genome can Be stored in 3Gb, a single mammogram requires 120MB, a 3D MRI 150MB, a 3D CT-Scan 1GB. Image Credit: NetApp

We already have plenty of HealthCare related data (think about radiography, blood tests,…) but this is basically nothing if we look at what we can expect in the next decades. My bet is that people living in the second part of this century will accrue TB of healthcare related data from their birth through their life. The generation of these data is not a big deal: more and more sensors will harvest data, birth data will include the sequencing of the genome (just 3GB) plus the basic exams at birth that will create the digital self of the baby (probably some 100 GB to start with). That digital self will keep growing accumulating historical data provided by a variety of sensors, both ambient, contact and embedded sensors. There might be less need to actually take a medical exam in the future since each person will be continuously subject to monitoring. That, of course will mean more and more data.

The question of course is: what can you do with those data? In principle, the more data are available the better, provided you can sort out meaning from them. Here is where artificial intelligence applied to the digital twin is needed. Deep learning algorithms will be able to distinguish what is normal for that person/digital twin from what should raise a red flag.

All of this covers the first point: achieving a better understanding of that specific person health status and forecasting potential problems (predictive analytics is expected to save 25% of healthcare cost by 2023).

As for the second direction already today a medium size hospital is accruing hundreds of TB of data each year. The growing adoption of electronic medical records all over the world is fuelling the transition to the digital transformation of healthcare.

The possibility to compare data across patients and more generally cross checking healthcare data of million of people is going to change the whole healthcare system.

The complexity of data management and of the interactions among different systems (including robot -soft and hard- based healthcare) is pushing towards a symbioses of virtual and physical health care with a strong interplay of human doctors with virtual assistant in the cyberspace (like Watson).

At present, concerns over privacy (and in the future over hacking of body sensors and actuators) are slowing down the healthcare digital transformation. At the TTM, Technology Time Machineand at the SAS Workshop in conjunction with TTMthis will be one of the themes being discussed.

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