Applying AI – A More Detailed Look at Healthcare Diagnostics

This is intended to be a slightly more detailed look at a single vertical or domain – Healthcare, and within this the single area of diagnosis support, and how ‘new AI’ in different guises is being applied, and by whom, and in what way, and to what end.

Its OK to say that the potential for a ‘marriage’ or liaison between Healthcare and AI is no closeted secret. Healthcare is large, complex, and deeply encased in centuries of knowledge and empirical reasoning. The network relationships between physicians or doctors, institutions, patients, treatments and outcomes are a barely understood global resource of great potential value. The whole is too large for any single human to encompass. Inefficiencies or discrepancies in diagnosis and outcomes are inevitable. The potential for new technologies to help to disrupt and reshape the healthcare market and the diagnosis process is clearly understood. VCs are also clearly interested in the outcome, whether commercially or philanthropically; a good example is Vinod Khosla and the ventures his firm represents, from Lumiata (see below) to Ginger.io and CrowdMed.

Healthcare is of course a universe in itself. Diagnosis, Prescription, Monitoring, Intervention – each area or subsection has its own challenges, contexts and actors involved. The potential for ‘universal’ and non-invasive monitoring or sampling tools and applications is of course enormous in itself. The forecast explosion of consumer data-creating devices and applications is going to create a ‘stream processing’ event orders of magnitude beyond what exists currently. As stated, I’m going to try to concentrate here on Diagnosis, and on software rather than hardware, in the form of ‘expert systems’ to support or guide human decision making.

One place to start is with the ‘who’ rather than the ‘what’ or the ‘how’.

The IBM Watson ‘cognitive computing’ project has a valid claim to be an early starter, and also to be in the forefront of many peoples minds with the heritage of the ‘Deep Blue’ project and on to the 2011 ‘Jeopardy‘ demonstration and subsequent publicity generated. Back in the real world, Watson is now applied as solution as a ‘Discovery Advisor‘ in different domains – including healthcare for clinical trial selection, and pharmaceutical drug development. It’s an approach that is both ambitious and intensive – involving many years of intense R&D and the costs associated, and the partnerships with leading Physicians and Institutions including Cancer and Genomic research for ‘training’ over as many years on top of or outside of this. Outside of healthcare, the ‘Question and Answer’ approach is merged with other IBM product lines for Business Analytics and Knowledge Discovery. The recent acquisition of AlchemyAPI, a younger, nimbler technology and ‘outside focussed’ by its very nature, should integrate well to the Bluemix platform. The example below is from IBM development evangelist Andrew Trice, with a voice UI now for a Healthcare QA application:

Whilst I admire the ambition (whether commercially driven or not) and the underlying scale of Watson, I may question if the ‘blinkenlight‘ aura generated by the humming blue appliance linked then to a ‘solutions and partner ecosystem’ notorious for tripling (or more) of any proposed budget will lead to a true democratisation. I feel the same unnatural commingling of awe and fear in response to the Cray appliance use cases. Amazing, awesome, and yet also extremely expensive. I guess the alternative – commodity hardware run at scale using a suitably clever network engineering process to distribute computation and process results- doesn’t come cheap either.

Also, I understand and concur with the need for ‘real stories’ that publicise and demonstrate an application in a way that the ‘average Joe’ can understand. (My personal favourite is the Google DeepMind Atari simulation – more elsewhere on this.) Some attempts, however well intentioned, simply don’t work, or at least in my opinion. The Watson-as-Chef and Food truck for ‘try me’ events makes me think ‘wow, desperate‘ rather than ‘wow, cool’.

The fast-paced improvement and application of ‘Deep Learning’ Neural Networks in image classification have opened up a new opportunity in Medical Image analysis. Some ‘general purpose Deep Learning as a Service or Appliance’ companies  such as  ErsatzLabs offer their tools as a service, and include Healthcare diagnosis use cases in their portfolio.

Enlitic.com proposes to offer a more ‘holistic’ approach combining different technologies and approaches – here both Deep Learning for Imaging and intriguingly combine this also with NLP and semantic approaches for healthcare diagnostics.

Lumiata‘s approach appears more graph-driven, ingesting text and structured data from multiple ‘sources’ from insurance claims, health records, and medical literature, creating an analytics framework for assessing or predicting patient ‘risk’, and exposing this as a service for other healthcare apps.

Its also worth mentioning Google, a potential giant of any domain if desire exists, who have already made a move in to health, leveraging their dominance in search and status as ‘first point of call on the internet’ to provide curated health content, including suitably gnostic pronouncements on search algorithm ‘tweaking’ to support this curated health service.

In terms of diagnosis and treatment, the ‘data types’ currently being referenced are essentially images, text or documents, including test results, and relationships. The technical approaches applied map closely to these – Deep Learning Nets for classification of imaging, NLP / XML for semantics, ontology and meaning in unstructured documents and text, and Graph Analytics at scale for the complexity of the ‘web’ of patient-doctor-diagnosis-disease-treatment-outcomes.

Two of the example companies discussed here – IBM Watson and Cray – have a heritage in the high-end (read expensive) appliance or super-computer systems architecture for running highly memory and processing intensive real-time analytics at scale, and the expensive hordes of suited consultants to implement, deploy and manage these solutions over time. The other, newer, smaller ventures show mixed approaches, and, although its early days for any publicly available data, I would assume on a more ‘flexible’ commercial basis.

So what’s the big story? Stepping back and looking down, healthcare and the data it consists of seems to me a big ‘brain’ of information constructed from different formats and substances, but linked together in complex relationships and patterns hidden or obfuscated by barriers of format and location or access. This is traditionally referred to as the ‘real world’, whether its Healthcare or the Enterprise.

The goals or objectives can be simply phrased – improving and optimising patient outcomes, and placing the patient at the centre.

The adversarial paradigm of ‘bad AI’ of rapidly-evolving software systems ‘competing’ against physicians  in a winner-takes-all for the right to diagnose and treat patients is of course naive. And yet the healthcare industry is clearly labelled and targetted up for a ‘disruption’ in the coming decades in terms of who does and is responsible for what. Whatever this ends up looking like, we can be sure it will be radically different to the way it is now.

Its a big, big challenge. No one venture – even at the scale of Google or IBM – is going to do this by themselves. Its going to rely also on a host of smaller ventures, but ones with inversely large ambitions.

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