Will Artificial Intelligence Revolutionise Eye Healthcare?

Nick Mandella our consultant managing the role
Posting date: 9/26/2018 1:32 PM
Faced with a rapidly expanding and increasingly older population, Healthcare resources in both the UK and US are facing an unprecedented level of demand. With only limited resource available, conversation is beginning to turn to the potential use of Artificial Intelligence (AI) to ease some of the strain.

A recent example already seeing success is the current collaboration between Google’s DeepMind and London’s Moorfields Eye Hospital. But, as the lines begin to blur between human and machine-diagnosis, it’s worth questioning what role AI should actually play. 

SEEING THE POTENTIAL IN AI


Aside from the increase in population, there are many societal elements that are affecting the healthcare system. An increase in illnesses such as diabetes has led to a rise in eye-diseases and increased demand on optometrists. 

Fortunately, AI can speed up the process with new technologies allowing systems like DeepMind to make their own diagnosis.

Optical Coherence Technology (OCT) allows optometrists to create a 3D scans of people’s eyes. By bouncing near-infrared light of the interior surfaces of the eye, it can create an image that will reveal any abnormalities. DeepMind has been trained on over 15,000 scans and can now form a likely diagnosis, having used algorithms to find common patterns within the data. 

Head of DeepMind, Mustafa Suleyman, says:

“ [This could] transform the diagnosis, treatment, and management of patients with sight threatening eye conditions [...] around the world.”

However, with an accuracy of just over 94%, there is still enough room for error to cause concern, especially given the potential consequences of an incorrect diagnosis. 

LOOKING FOR MISTAKES 


This doesn’t mean we should rule out the use of AI altogether. Whilst we may not be able to solely rely on the technology for diagnosis, it can be effective when working hand-in-hand with a human skillset. 

In particular, by using AI systems for Triage purposes (determining what order patients should be seen in), as opposed to making a full diagnosis, patients demonstrating more significant symptoms could be reported and seen by a medical professional as priority, potentially leading to a higher chance of recovery. 

When AI is used as a driver for patient management, as opposed to being viewed as alternative physician, it can create a faster and more efficient process. 

To help continue to improve the results produced by DeepMind, the NHS have been given a validated version to use for free for the next five years. Using real-world applications over this time should streamline both their processes, and the technology itself. 

A LONG TERM VISION


For the time being, AI’s role within Eye Health is one of evolution, not revolution. With the inconsistency of current technology and the impact of incorrect results on people’s sight, it can only be utilised as a supporting tool. 

For now, the skillsets of Data Analysts and medical doctors remain too separate to full work hand-in-hand. Add to this the risks of automation bias (a willingness to blindly trust a machine’s output), and the margin of error is too high. 

However, that’s not to say that AI can’t and won’t play a significant part in the future of Healthcare. With the technology to detect eye conditions through the lens of your smartphone camera closer than ever to mainstream use, AI is set to play a huge role in outpatient treatment. At this stage, however, that role will be one of risk predictor, not eliminator. 

If you think you have the skillset to help take AI to the next level in Healthcare we may have a role for you.

Take a look at our latest opportunities or get in contact with our team. 

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Visit our Blogs & News portal or check out the related posts below.

Defragmenting Data Analytics

This week's guest blog is written by Moray Barclay.                    Around 20 years ago I was showing some draft business plans with cashflow projections to my new boss. His name was Marc Destrée and I concluded by saying I’d like to get the finance department involved. “No”, Marc replied. He paused for several seconds, looked up from his desk, and explained "Do the internal rate of return. Then we discuss. Then we give it to finance." He was right of course, for three reasons which together represent best practice. Firstly, it cemented the separate accountabilities between the different job functions responsible for the business case and financial governance. Secondly, there were no technical barriers to separating the “cashflow creation process” and the “P&L creation process” as everyone in the organisation used the same product: Excel. Thirdly, it assigned the right skills to activities. Today, organisations have no equivalent best practice upon which to build their data analytics capability. The lack of best practice is caused by fragmentation: fragmentation of job functions, fragmentation of products, and fragmentation of skills. This is not necessarily a bad thing: fragmentation drives innovation, and those organisations who get it right will gain huge competitive advantage. But the application of best practice mitigates against unnecessary fragmentation and hence unnecessary inefficiencies. So how could best practice be applied to an organisation’s data analytics capability? In other words, how we do defragment data job functions, data products and data skills? Defragmenting data job functions A good starting point to understanding best practice for data job functions is the informative and well-written publication “The scientist, the engineer and the warehouse”, authored by the highly respected Donald Farmer of TreeHive Strategy. He includes references to four job functions: (i) the data scientist, (ii) the data engineer, (iii) the business intelligence analyst and (iv) the departmental end user.  (i) The data scientist: The accountability of the data scientist is to build data science models using their skills in maths and coding to solve business problems. In addition to using open source technologies, such as python and R, data scientists can and do use data science platforms such as Knime which enable them to spend more time on maths and less time on coding - more on data science platforms later. (ii) The data engineer: The accountability of the data engineer is to build robust and scalable data pipelines which automate the movement and transformation of data across the organisation’s infrastructure, using their skills in database engineering, database integration, and a technical process called extract/transform/load (ETL) and its variants – more on ETL production platforms later. (iii) The business intelligence (BI) analyst: Donald Farmer’s publication does not address the accountabilities of the BI analyst in any detail because that is not its focus. Unlike the clearly defined roles of data scientists and data engineers, there are no best practice descriptions for the role of BI analyst. Typical accountabilities often include designing data visualisations from existing datasets, building these visualisations into reports or online dashboards and automating their production, and configuring end users to ensure they only have access to data that they are approved to see. Beyond these core accountabilities, BI analysts sometimes create entirely new datasets by building complex analytic models to add value to existing datasets, using either a suitable open source technology (such as python, but used in a different way to data scientists) or a data analytic platform such as Alteryx which enables the creation of code-free analytic models. One final point - a BI analyst might also build data science models, albeit typically more basic ones than those built by data scientists. BI analysts will inevitably become more like data scientists in the future driven by their natural curiosity and ambitions, vendors creating combined data science platforms and data analytic platforms, and organisations wanting to benefit from the integration of similar functions. (iv) The departmental end-user: A departmental end-user is generally the most data-centric person within a department: it might be a sales operations professional within a sales department for example. I am told that when Excel was first introduced into organisations in the 1980’s, there would be a “go-to Excel expert”; self-evidently over time everyone learned how to use it. I was there when CRM systems like salesforce.com and Netsuite appeared 20 years later, and the same thing happened: initially there would be one or two pioneers, but eventually everyone learned to use it. The same democratisation is happening and will continue to happen with business intelligence. In the same way that CRM and Excel are used by everyone who needs to, soon anyone will be able to build their own data visualisations and reports to help identify and solve their own problems. In some organisations such as BP this is already well-established. And why stop there? If a departmental end-user can model different internal rates of return and create visualisations, then why should they not apply their own data science techniques to their own datasets? But this can only happen if the role of the BI analyst has an accountability for democratisation, in addition to those mentioned earlier.In summary, the following is a list of best practice accountabilities for the BI analyst: (1) Build and automate the initial set of business intelligence reports and visualisations (2) Create the data governance framework to enable self-service by departmental end-users (3) Act as the initial go-to business intelligence expert (4) Evangelise a data-driven culture and mentor those who want to become proficient in self-service (5) Deploy resources which over time make redundant the role of a go-to business intelligence expert (6) Over time, increase time devoted to creating innovative datasets by building complex analytic models which add value to existing datasets - using open source technologies and/or a data analytic platform (7) Work with the data science function in such a way that over time the data science function and the BI function can be merged The above best practice eventually results in the role of the BI analyst, or the BI analyst team, becoming redundant, much in the way that the role of a dedicated Excel specialist died out in the mid-1980’s. As mentioned earlier, as BI analysts will move into data science, this should not result in people losing their jobs.  Defragmenting data products Unlike open source technologies there is a highly fragmented data product landscape. Products include data science platforms, data analytic platforms, platforms which are more visualisation-centric, and platforms which are more focused on data governance. There are also ETL production platforms which are in the domain of the data engineer but which include functionality to build some types of analytic models. Fragmented markets eventually consolidate. Even the broadest three cloud vendors, Amazon, Google and Microsoft, do not cover the entire landscape. For visualisation there is Quicksight, Data Studio, and Power BI respectively as well as competitive products, most obviously Tableau; for ETL production platforms there is Athena, Cloud Dataflow and Azure Data Factory, as well as competitive products such as Talend. But smaller vendors have the lead in data science platforms and data analytic platforms. The hiring by Microsoft of the python inventor Guido van Rossum two months ago points to their ambitions in data science platforms and data analytic platforms. Market consolidation in 2021 seems inevitable, but the details of actual acquisitions are not obvious. After all, it was salesforce.com which bought Tableau in 2019: not Amazon, Google or Microsoft. Best practice for organisations is to consider possible vendor consolidation as part of their procurement process, because product fragmentation means there is a corresponding fragmentation of skills. Defragmenting data skills Fragmentation of data skills means that the market for jobs, particularly contract jobs, is less elastic than it could be. The fragmentation of skills is partly caused by the fragmentation of products and their associated education resources and certification. Vendor’s product pricing typically falls into three categories: (i) more expensive commercial products (c. £500 - £5000 per user per month) which include free online education resources and certification; (ii) inexpensive commercial products (c. £5 to £50 per user per month) which usually require a corporate email address but have free online education resources and reasonably-priced certification exam fees (c £100- £200); and (iii) products which are normally expensive but have an inexpensive licensed version that cannot be used for commercial purposes, again including free online education resources and certification. The latter approach is best practice for solving the fragmentation of skills because the barriers to learning (i.e. high product cost or the need for a corporate email address) are removed. Best practice includes the Microstrategy Analyst Pass, which is available to anyone and costs $350 per year including a non-commercial product licence, online education resources and access to certification exams. University students (as well as self-educated hackers) learn open source technologies and one would expect that those skills are sufficient for them to enter the workplace in any data analytics environment. Yet several vendors who provide the more expensive commercial products (c. £500 - £5000 per user per month) and do not have discounted licences for non-commercial purposes make one exception: universities. At face value, this seems benign or even generous. But it contributes to the inelasticity of the job market at graduate level because an unintended consequence is that some graduate data analytics jobs require the graduate to be competent in a product before they have started work. Best practice is for organisations to employ graduates based on their skills in maths, statistics and open source technologies, not product. In seeking corporate acquisitions, vendors might find that their customers value “education bundling” as much as “product bundling”. Customers who are happy to pick, for example, the best visualisation product and the best data storage product from different vendors might be more attracted to their people using a single education portal with the same certification process across all products. And if an organisation can allocate 100% of its education budget to a single vendor then it will surely do so. Best practice is for vendors to consider the value of consolidating and standardising education resources, and not just products, when looking at corporate acquisitions. Defragmentating data analytics The consequence of implementing a best practice data analytics capability based on the principles of defragmentation has profound consequences for an organisation. It enables a much richer set of conversations to the one which took place 20 years ago. A young business development manager is showing some draft business plans to their new boss. They conclude by saying they’d like to get a data scientist involved. “No”, the boss replies. He pauses for several seconds, looks up from his desk and explains "Segment our customer base in different ways using different clustering techniques. Then run the cashflow scenarios. Then we discuss. Then we give it to data science." You can view Moray's original article here. Moray Barclay is an Experienced Data Analyst working in hands-on coding, Big Data analytics, cloud computing and consulting.

Getting Ahead As A Specialist In Data Science

For professionals working in Data Science, the discipline is all about discovery, insights and innovation. Rapid advancements in the adoption of data and technologies, coupled with organisations feeling the strain of the mass of data they have, means that Data Scientists are in high demand. To stay ahead of the competition, companies must continuously look for unique ways to extract insights from the large volumes of data they acquire. This is where professionals from Data Science come in. Their skills lie in correlating data points, mapping out trends and identifying insights that support organisations to action change and/or enhance their growth. Now more than ever, due to the global pandemic, opportunities to move into a career in this space are vast. From Data Scientists, to Data Engineers and Heads of Analytics and Machine Learning, the possibilities for professionals in this discipline are limitless. Here are just a handful of the things you should know for a career in Data Science. Data Science is supporting the future According to the 2020 Emerging Jobs Report published by LinkedIn, the role of the Data Scientist continues to be an incredibly important one within data, analytics and technology. It also shows that, in our core markets, this position continues to be one of the top emerging roles: USA, it is thirdUK, it is seventhGermany, it is eighthFrance, it is tenth Data Science is a discipline that is growing. In the past year, we have seen these professionals demonstrate their ability to adapt into industries such as retail, banking and medicine, where we have seen such sharp change in consumer habits and a step up in global demand. They are all poised and ready to make use of Data Science functions and analytics. Take healthcare and medicine, for example. New collaborations, funding routes and systems for sharing data will shape research from now on. Data supports the way in which we interpret information and provides a means for us to make predictions, spot new trends and developments as well as better managing supply chains and organisational planning. Taking on a role as a Data Scientist or engineer will ultimately be a purpose-driven career, driving future innovations and making a range of business processes simpler and more effective. Professionals require an array of skills The way in which we have become connected today is ubiquitous. It is this level of connectivity that is having a direct impact on the way in which organisations operate (and how their consumers make use of services), due to the growing levels of data that are being collected and are then required to be interpreted and managed. Professionals working within the realms of Data Science need to keep this link in mind as their career and new project opportunities arise. Whilst it is crucial for these specialists to have unique skills such as Apache Spark, Data Science, Machine Learning and Python, they also need to have a clear understanding of statistics, hold business development skills and be clear communicators. Particularly of Data Scientists and engineers, it is imperative to really understand what the customer is looking for from the software, be able to handle multiple projects side-by-side and have excellent end-to-end experience across relevant frameworks. Professionals should take the time to bolster these skills, particularly for technical needs, by completing ongoing online courses, speaking to industry experts and staying updated with the latest iterations of programming and data languages. There are a range of career opportunities for skilled, savvy Data Science professionals with an interest in data and analytics on the table. The discipline is determined by technology and trends, making for a dynamic, rapidly developing industry that is growing at an unprecedented rate. Critically, as the industry continues to advance and demand for skilled professionals grows, there will be plenty of opportunity for you to make your mark. If you’re a Data Scientist looking to take a step up or are looking for the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

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