AI has massive use potential in healthcare. However, most organizations also possess plenty of unstructured sensitive data that is not centrally stored nor managed.
It could help e.g. in Predicting who might be developing cancer five years from now; in Preventing human errors in diagnostics; in Detecting a certain disease faster within x-ray images; and in assisting in the treatment or medication of already sick patients; and even helping out with the Response – advanced brain surgery.
For advancing AI and its usage, the adoption of the cloud is critical. AI requires massive computing power, and using the cloud is the only viable option to reap full benefits of AI.
However, in the Nordics, unlike the rest of the world, there seems to be a bit of resistance to AI. This is on the one hand consumer related, as shown in a Tieto survey regarding AI. For the public sector, however, there is also the legislation perspective regarding the use of public cloud services. So, it would seem that there is this double whammy for adopting AI and the cloud in the public sector.
What is the problem? Nowadays, the security and data protection capabilities of the cloud are at least equal to, if not even better than, those of traditional on-premises systems.
And what’s really the deal with AI and Machine learning? According to just one estimate, the global investment in AI and Machine learning is massive and is expected to reach $6.6 billion in 2021. All the major public cloud providers, such as AWS, Google, IBM and Microsoft, invest heavily in this area and so do their business partners who utilize their cloud ecosystems, partners like Tieto.
In Cybersecurity we usually talk about this area as providing capabilities or mechanisms within security products to be able to Predict, Detect, Prevent and Respond to Cyber Security Incidents. As discussed in a previous blog post, in cybersecurity AI and Machine learning are being used to Predict what might be coming your way by collecting and analyzing massive amounts of data; to Prevent the most sophisticated malwares targeting our customers networks, and to Detect anomalies on workstations or servers helping with the actual Response to the threat.
It is quite interesting to use the same analogy for the public healthcare sector. AI and Machine learning can help with a whole bunch of areas in healthcare which would benefit from the fast analysis of massive amounts of data.
As a security services provider, I wouldn’t want us, Tieto Security Services, to support our customers with less advanced tools than the competition. In a similar fashion, I would want the doctors, researchers, nurses or healthcare workers in the Nordics to have access to the best possible technology advancements. I want them to be able to be proactive, to get help sorting out the signals from the noise, focusing on what is really important – saving people’s lives.
Which bring us back to the topic of the cloud. Do the challenges in using of the cloud and AI in the public sector, especially healthcare, boil down to attitudes and legislation? The integrity of patient data (or any personal data, for that matter) is of course crucial and must be 100-% certain, but the cloud is nowadays up to the job.
What should be done about the two areas which a seem to be the main stumbling blocks for the public sector in reaping the benefits of the cloud and AI?
If this, and secure cloud adoption, are something you wish to discuss further, please contact me or our team for further information.
Peter has a long track record of helping businesses increase their security posture. With a curious mindset and a geek's mentality towards technology, Peter helps customers navigate through the enormous security landscape to achieve the best possible outcome. This curiosity led to a deep dive into GDPR and the many challenges our customers and their consumers face, to better understand and advise on how security can play a supportive role in order to obtain compliance. Peter has a background from companies such as F-Secure, Atea as well as Nordic startups.