An unspoken promise of all technological progress is that the average citizen’s life will improve greatly from its current level. The improvement of life quality is an explicit promise of the current AI summer, and in particular machine learning has promised to improve and lower the cost of medical care. Companies such as Babylon Health have effectively promised the patient a low cost personal physician. Over the course of this blog post we will speculate about the near and immediate future based upon the current research being conducted in academia and industry in the area of medicine and wellness.
We know that our health is dependent upon our diet. Research has shown that a balanced diet and moderate exercise is a protection against a number of late life diseases such as cancer and dementia. However with busy lifestyles where food preparation speed is favoured over good quality food, and misleading food labelling makes it difficult to have a balanced diet. There is now a movement with the machine learning community to develop food recommendation systems that actively recommend food that protects against specific diseases . It is not a far fetched idea that personal digital assistants on mobile devices can produce personal diet plans which maximize the health of the user. And with automated ordering it would be possible for them to order the food and monitor the health outcome through sensors embedded in the household.
The food as medicine is being supported by another area of AI, knowledge representation also known as semantics. Pressure from organizations such as the USDA for better nutritional information has pushed scientists to produce knowledge graphs or ontologies of food. These ontologies describe food and food products in a standard way, and therefore can be used in machine learning algorithms and in the near future automated cooking systems such as Thermomix. The start of this revolution can be seen on the bbc website where recipes are published in JSON-LD.
Food as medicine is a cheap and effective way of combating disease, however AI is not limited to the mundane ordering of food, and is being used as a diagnostic tool.
Mobile phones can be at the centre of personal health care. It is not a massive leap of the imagination to visualize a personal AI physician sitting on a mobile phone, and asking patients to submit photos of various parts of their bodies so that diseases can be detected. It is possible to predict diseases from images such as Alzheimer’s disease and skin cancer therefore the AI physician could suggest the patient submit photos based upon their lifestyle or risk profile, such as if a patient has booked multiple sessions on sunbeds or has been exposed to excess Ultra Violet. The risk could be estimated by the AI physician from sensors on the phone, GPS data and Internet purchase history. This preemptive scanning and early diagnosis would prompt early medical intervention which always has better outcomes than later diagnosis.
The ultimate goal of an automated AI physician would be not to detect disease, but to recommend treatment. And if possible automatically order and supervise the consumption of medication. And again with current technology it is not a huge leap to imagine a fully automated system on a mobile device that is constantly monitoring the user’s health and intervening when necessary.
The imagined utopia of the near future is not limited to a personal physician. Cures for the major diseases may be here sooner than we can imagine. It is not a wild claim to state that we will be entering a golden age of drug discovery which is being driven by deep learning. Traditionally new drugs were discovered through researcher intuition and long laborious tests in wet labs. Discovery of new drugs therefore is a decade long process. Healthcare AI and deep learning has presented a shortcut where large numbers of drug candidates can be simulated in a computer and tested for their theoretical properties and the most promising candidates are then handed off to the wet lab researchers for evaluation. This process takes luck and time out of the equation in the drug discovery process. Therefore in our imagined health utopia that there will be drugs designed and discovered by AI that will offer relief and possible cures for the major diseases. This is not a forlorn hope, and already there drug candidates such as BPM31510, an anti-cancer candidate which was discovered by an AI process, is in phase two trials. In our imagined health utopia there will be possible treatments for diseases which are at the moment nothing short of a death sentence.
Technology often offers false promises, but this is not the case with AI and medicine. AI can help with disease prevention, detection, treatment as well as drug discovery. These techniques applied at a large scale will have a significant impact on a population’ s health. It is possible to imagine that AI assisted medicine can not only extend people’s life expectancy, but their quality of life as well. The only roadblock on the horizon is that of ethics and privacy.
Invasive AI personal physicians have the potential for abuse. For example will insurance companies demand access to the records and inferences made by these pocket doctors to deny insurance cover? And there is the human factor of accepting life changing decisions from technology. No doubt mistakes will be exaggerated as we have seen from the press around accidents involving self-driving cars. Is this imagined health utopia just around the corner? It is not, but the trajectory that current deep learning and machine learning is on, is not that far away, and the promise of automated medical agents as promised by science fiction may start to be realized during the lifetimes of the current machine learning practitioners.
If you would like some help to find out what AI can do to help you and to find out what we have already built in the field of AI healthcare, have a look at our data science consulting services.
From finance to healthcare, from market research to media monitoring, we can help your people make better decisions. We work alongside companies like yours to help deliver successful AI and ML projects - to make a real business value impact
The challenge: Deloitte’s partners and account managers found they were drowning in news from sales support teams, and unable to react quickly to market changes
The solution: Deeper Insights built a prototype Automated Insights app allowing them to have better conversations with clients and close more business
The outcome: Account managers at Deloitte close more business thanks to actionable insights delivered straight to their phones
Client said: "There are a number of gems we’ve found that are far better than the standard services we use" - Dimitar Milanov, Partner, Deloitte
The challenge: Help the sales and marketing teams know more about their customers to enable them to drive deeper customer engagement in sales meetings
The solution: Deeper Insights developed a CMS that scraped the web and automatically identified and summarised customer events relating to key accounts at JLL
The outcome: The automation of the whole previous manual process, and being able to identify 60% more news stories than the manual process, enabling JLL have better and more informed conversations with their clients
Client said: "We have lots of researchers and people who generate insights for our clients, Deeper Insights™ (formerly Skim Technologies) helped us improve the speed at which we get insights and have better conversations with our clients." - Chris Zissis, CIO, Jones Lang LaSalle
The challenge: In the UK, the total number of Total Knee Replacements(TKA's) per year has increased from 13,546 in 2003 to 98,147 in 2019 costing the NHS an estimated £585m per year. The average cost of a TKA in the UK is £12,000, however, post-surgical complications, e.g surgical site infection, increases this cost by between £1618 and £2398 per patient.
The solution: Our consortium, which is comprised of Smith&Nephew Ltd, Deeper Insights and Imperial College London, won Innovate-UK funding to carry out an ambitious and innovative project that is focussed on developing markerless and automated registration and tracking of the patient's limbs tailored for robotic-assisted orthopedic procedures using structured light technology assisted by deep learning to continuously capture the patient's anatomy during surgery.
This new platform will be integrated within S&N's commercially available robotic platform "NAVIO," which was previously supported by I-UK funding, and will obviate the need for percutaneous markers reducing set-up time, cost and complexity during surgery.
Discuss your AI project with us and lets see if we can help. We can dive into the data you have, the data we can gather from the web and other data sources, how we can manipulate that data for you and how we can output it in a dashboard that your business can actually use.
Our Data Science experts are recognised globally with over 500+ citations and patents
We have a combined experience of over 40 years in developing cutting edge, and innovative Artificial Intelligence from both academia and industry. We are specialists in Computational Linguistics, Natural Language Processing, Machine Learning, Deep Learning and Data Analytics
Dr Márcia Oliveira,
PhD Network Science
Dr Claudio Sa,
PhD Deep Learning
Dr Catarina Carvalho,
PhD Image Processing