• AI
  • Machine learning use cases for health points to the future

    Machine learning (ML) and artificial intelligence (AI) have quickly rocketed to the top of the industry’s buzzword list, driven partly by heightened interest in big data analytics amongst healthcare providers and vendors

    The allure of intelligent algorithms to mine masses of structured and unstructured data for innovative insights get’s health planners pretty excited. However, a fragmented health ICT landscape and sluggish analytics development have thus far kept that Holy Grail beyond reach.

    Regardless, ML is already making a difference.  Here are some examples;

    Imaging analytics and pathology

    ML can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly and potentially leading to earlier and more accurate diagnoses.  At Stanford University, ML tools performed better than human pathologists when distinguishing between two types of lung cancer.  The computer also bested its human counterparts at predicting patient survival times.

    Natural language processing and free text data

    Using natural language processing (NLP), ML algorithms can turn images of text into editable documents, extract semantic meaning from those documents, or process search queries written in plain text to return accurate results.  Anne Arundel Medical Center is using a natural language interface, similar to any of the widely known internet search engines, to allow users to access data and receive trustworthy results.

    Clinical decision support and predictive analytics

    Identifying and addressing risks quickly can significantly improve outcomes for patients with any number of serious conditions, both clinical and behavioral. The University of California San Francisco’s Center for Digital Health Innovation (CDHI) and GE Healthcare are creating a library of predictive analytics algorithms for trauma patients in an attempt to speed up the delivery of critical care.

    Cybersecurity and ransomware

    At the end of 2016, IBM Watson launched its Cyber Security Program.  Watson’s ML and cognitive computing skills are used to flag cyber threats and check for suspicious activity against known malware or cyber crime campaigns.  This helps IT staff take better decisions based on known characteristics of malware.

    ML and AI are the keys to addressing health care inadequacies.  These technologies can help predict and control disease, expand and augment service delivery, and address several persistent social inequities. Ubiquitous health tech is by no means inevitable.  Successful rollout will entail an immense amount of concerted effort, capital, labor, and partnership.

  • How can Africa’s eHealth afford AI?

    As AI creates more potential and opportunities for better health and healthcare, it’ll need more investment in ICT. It’s a core theme of an eBook by Azeem Azhar, editor of the weekly blog Exponential View. For Africa’s health systems, it looks like an emerging and major component of their eHealth strategies. 

    Published by Medium, ArtificiaI Intelligence and the Future of Computing sets out his view of the future where machine learning creeps across enterprises and increases the demand for processing capacity significantly. Three main impacts on the ICT supply side, its hardware and software suppliers, practices and opportunities include:

    Considerable in the computing capacityFlourishing cloud servicesNew chip species.

    These are matched by users’ demand. They’ll need to invest too to pursue their AI goals and benefits. An indication of the scale of the ICT investment step-up’s Azhar’s comparison with Moore’s Law. It’s an average annual 60% improvement of transistor packing or performance every year, a significant expansion. He says the growth AI needs’ll be bigger than Moore’s Law.

    It’s not the only expansion. More algorithms and processing capacity are two. The third’s data, a component requiring Africa to increase its eHealth investment too. These work as a cycle. As processing power increases, more demanding algorithms are possible. These need more data, increasing the demand for more and better processing, leading to more complex algorithms.

    These AI forecasts have considerable implications for Africa’s eHealth. They raise questions like:

    How can Africa afford AI investment and general eHealth to provide baseline data?How much will it have to invest in human capacity and capability for AI and its algorithms?Within low healthcare budgets, how long will it take?What’s the relative strategic requirement for AI opportunities compared to other eHealth and initiatives such as IoT?

    Simple questions. No easy answers.

  • AI helps to identify schizophrenia

    Artificial Intelligence (AI) is on the move. It’s becoming an integral part of healthcare. Research by IBM and the University of Alberta published in a report in the Nature Partner Journal Schizophrenia, a Nature publication, shows that machine learning algorithms can predict schizophrenia with 74% accuracy, says an article in IT-Online.

    It also shows that the technology can predict the severity of patients’ specific schizophrenic symptoms based on correlations observed across different brain regions. This predication of symptom severity could help clinicians identify customised treatment plans for each patient.

    Schizophrenia’s a chronic neurological disorder affecting up to eight out of every 1,000 people. Patients experience hallucinations, delusions and cognitive and physical impairment.

    Dr Serdar Dursun, professor of Psychiatry and Neuroscienece at Alberata University’s optimistic that the new technology’s. “Innovative multidisciplinary approach opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease.”

    The study can also extend these techniques to other psychiatric disorders such as depression or post–traumatic stress disorder, both of which are often tricky to diagnose and harder to treat. 

    Using AI is one way that Africa’s stretched mental health services can expand their impacts. The research provides a justification for it as part of eHealth strategies.

  • Microsoft's joined the AI race

    The Artificial Intelligence (AI) supply side’s heating up. An article on the BBC says Microsoft is promoting its AI credibility and visibility. It brought its top scientists from across the world to London demonstrate their ideas, vision, research, initiatives and AI’s direction that over the next few years.

    One project’s Seeing AI. It does several clever things. It helps people with visual impairment access information using a smartphone’s camera. Pointing a phone at documents enables it to read them aloud. A set of bleeps guide people to barcoded on drinks cans to tell users what it is. 

    Creating Seeing AI took several years. Microsoft’s AI research programme’s been underway for 25 years. It has three main parts, speech, language and vision. Some of it’s now coming to fruition and seeing, recognising and understanding our world in a similar way we do. 

    Microsoft’s AI progress adds extra opportunities for Africa’s consumers and health systems. When will they become part of Africa’s eHealth strategies?

  • Luckily, it wasn’t treating patients

    As AI takes control and robots push us to one side, can we really rely on these smart machines? Maybe we can, if we allow them some degree of human frailty. 

    A security robot drowned itself by diving, or maybe only stumbling, into a shopping centre and office block’s ornamental pool in aptly named Georgetown Waterfront, pun intended, Washington DC. Fortunately, it didn’t make it through medical school, so wasn’t operating on patients at the time.

    The Knightscope security robot bid farewell to all around, and added a poignant extra meaning to Shakespeare’s anguish expressed by Hamlet:

    “For in that sleep of death what dreams may come

    When we have shuffled off this mortal coil,

    Must give us pause: there's the respect

    That makes calamity of so long life.” 

    If it senses a threat, it can squeak, whistle and make other loud alarming noises to deter criminals and nuisances. It can withstand attacks too. It can’t swim. It rejected John Lennon’s advice, that “When you're drowning, you don't say 'I would be incredibly pleased if someone would have the foresight to notice me drowning and come and help me,' you just scream.” Eye witnesses said they weren’t sure if it was waving, drowning or just cooling off.

    It looks like it’s back to the drawing board. Maybe it should be a surf board.

  • Africa’s eHealth isn’t far behind South America and Mexico

    Africa’s eHealth has a different profile to the average for South America and Mexico, but it’s not trailing significantly. Comparing findings from the WHO and GEO’s Global eHealth Survey 2015 shows similar simple coverage rates of just over a third. A global average’s near 50%.

    Perhaps the most important difference’s the emphasis on Big Data. South America and Mexico are at about 12%. Africa’s at about 2%. The survey didn’t ask for data about Artificial Intelligence (AI) or coverage of cyber-security. It doesn’t provide data about the quality, sophistication or maturity of the coverage. It’s not easy data to assemble, quantify or analyse. 

    Suffice to say, it highlights the need for Africa’s health systems to catch up on AI. A first step may be using their existing data more extensively. A second may be supporting public health specialists and clinical teams in local AI initiatives. The comparison seems to indicate no need for a sudden rush. A steady, imaginative plod along the AI road should be sufficient.

  • eHealth's AI can benefit healthcare’s operational activities

    Realising benefits from eHealth’s the essential goal. These can be for patients, communities, health workers, healthcare providers and health systems. AI in eHealth’s changing the opportunities. 

    A report from Tableau Software, based in Seattle, sets out four ways that AI benefits healthcare. Four ways data is improving healthcare operations says, under the over-arching goals of lower cost and better care, they’re:

    Enabling population health managementIncreasing productivityAggregating and blending data to reveal supply chain inefficienciesAutomating visual analysis for better revenue cycle management.

    Achieving these depends on an appropriate eHealth approach. Tableau says the biggest problem’s not acquiring more data, it’s how healthcare organisations simplify their data to a point at where it’s not a technical construct. This leads to decision takers easily understanding where their organisations are, then identifying and moving in a common, appropriate direction.

    This may be true for US healthcare, but Africa doesn’t have comparable data volumes yet. An important lesson for Africa’s health systems seems to be to pursue parallel investment tracks of simultaneously making better use of its data and implementing more eHealth solutions.

  • Bouy determines a person’s medical condition

    Doctors and computer scientists in Boston and New York have developed Buoy, a free AI platform. It helps people to use their symptoms to determine their medical conditions and make better decisions. The eHealth tool began in 2014 at the Innovation Laboratory at Harvard. Buoy’s co- founder and CEO, Andrew Le says currently, medical information provided by simplistic web symptom checkers are often risky and unreliable. To overcome these limitations, Buoy leverages advanced machine learning algorithms to provide personalised and accurate analyses and diagnoses to users so they can quickly and easily have more control of their healthcare.

    Bouy asks users to enter their ages, genders, and symptoms. It then asks a few questions, such as the severity of their symptoms and their durations. It uses this information to analyse against millions of medical records to generate other important, more specific questions. After two to three minutes of analysis, Buoy has an accurate and detailed understanding of users’ conditions. It will then recommend appropriate healthcare alternatives. If immediate treatment’s needed, it provides directions on how to connect with a nearby healthcare providers.

    An article in eHealth news says Bouy’s been through a battery of quality control tests. The result’s that it can accurately analyse a wide range of symptoms, such as common colds, abdominal pains and how a change of running shoes has created muscular or skeletal issues.

    The study tried to determine how Buoy interprets a cough compared the top five web-based symptom checkers. It examined 100 standardised cases involving 33 different diagnoses with severity ranging from life-threatening pulmonary embolisma to benign, normal cough. Prevalence was assessed too, ranging from rare histoplasmosis to common cold. Results were that Buoy’s analyses were 92% accurate as compared to WebMD at 56%, Healthline at 53%, Mayo Clinic at 38% and Isabel at 28%. Buoy has over 5,000 users and is available as an app on Apple store and directly from Buoy.

  • UK’s Babylon Health raises US$25m for AI for remote medical consultation

    Access to doctors in Africa’s a persistent challenge. There aren’t enough of them. For many African’s, the journeys are long and costly and queues are long. Artificial Intelligence (AI) may soon offer a solution. 

    Babylon Health, a UK start-up, has raised US$25m from AB Kinnevik, a Swedish investment group, to develop AI in its mHealth app for people to access medical advice. Demis Hassabis and Mustafa Suleyman, the founders of Deepmind, are Babylon’s advisers. An article in the Financial Times says its new investment means it can hire scientists and computer engineers to develop an AI version of its app.

    Babylon’s a subscription health service already has 150,000 registered users. Sixty companies have incorporated it into their employee health plans. It’s also in talks with the UK’s NHS. Its app has several functions. Users can:

    Video conference with one of the company’s 100 doctorsMonitor healthBook appointmentsOrder medical tests.

    With the AI version, users can speak into their phone. AI in Babylon’s system then asks them a series of questions about symptoms to determine a course of action. The goal’s to establish a way to use people’s mobile phones to provide most of the healthcare they need straight to them. It’s the equivalent of a parallel version of Google providing information to users’ requests.

    Its role in healthcare is to screen patients. It doesn’t replace doctors with machines, but directs patients to health professionals when they need it. Tech Crunch (TC) reports that Babylon has curated the largest knowledge graphs of medical content. It’s part its advances in deep learning techniques adapted specifically for healthcare.

    Dr Ali Parsa, Babylon’s founder and CEO says “Artificial intelligence together with ever increasing advances in medicine means that the promise of global good health is nearer than most people realise. Babylon scientists predict that we will shortly be able to diagnose and foresee personal health issues better than doctors, but this is about machines and medics co-operating not competing. Doctors do a lot more than diagnosis: artificial intelligence will be a tool that will allow doctors and health care professionals to become more accessible and affordable for everyone on earth. It will allow them to focus on the things that humans will be best at for a long time to come.”              

    Babylon has two values, taught by experts, driven by data. Its AI technology’s taught by its own doctors providing trustworthy medical advice. People’s health data’s analysed continuously and cross-referenced with other patients to ensure they have the right results whenever they need them.

    This global goal’s welcome news for Africa.  It could be a huge step up for mHealth’s role in reaching and providing access to whole communities.      

  • AI and Big Data will transform healthcare

    There’s a substantial progression of digital capacity in which data is produced and stored. In 2013, the amount of available digital data encompassed 4.4 zettabytes and it is estimated to reach 44 zettabytes, which is 44 trillion gigabytes, or ten times more by 2020. There is no denying that the world of Big Data’s enormous. Healthcare’s challenge is how to make best use of it.

     Artificial intelligence (AI) is a vital and increasing part of it. It’s quickly becoming a necessity for healthcare. AI’s already widely used in everyday life. It’s in cars, Google searches, Amazon suggestions and other devices, but is yet to extend towards large-scale, routine AI for sophisticated activities such as healthcare and its clinical decisions.

    AI services such as Apple’s Siri, Microsoft’s Cortana, Google’s OK Google, and Amazon’s Echo can extract questions from speech using natural-language processing and perform limited set of useful things, such as looking for restaurants, provide  directions, find an open slot for a meeting, or run a simple web search. In addition, a 19-year-old British programmer launched a bot last September which is successfully helping people to appeal their parking tickets. It’s an “AI lawyer” that sorts received parking tickets. In both London and New York, the bot has a success rate of 64%, which translate to 160,000 of 250,000 parking tickets successfully appealed.

    The same efficiency is essential in healthcare. An article in Medical Futurist reports that in years to come, AI in healthcare and medicine may organise patient pathways and treatment plans, and provide doctors with all information they need to make better informed decision. Several companies have a stake in AI in healthcare. They include Dell, Hewlett-Packard, Apple, Hitachi Data Systems, Luminoso, Alchemy API, Digital Reasoning, Highspot, Lumiata, Sentient Technologies, Enterra, IPSoft and Next IT. Their ultimate goal is to transform medicine and healthcare in way that ensures that it’s widely available to the average, mainstream users and not only the richest medial institutions or to a handful of experts.

    AI’s used in several areas in healthcare. It includes mining medical records, designing of medical plans and medication management. Googles AI research branch in cooperation with the Moorfields Eye Hospital NHS Foundation Trust in London has launched a Google Deepmind Health project. It mines medical records to help to provide better and faster health services.

    This year, a British subscription, online medical consultation and health service launched Babylon. It offers medical AI consultation based on personal medical history and common medical knowledge. Users report the symptoms of their illness to the app, which checks them against a database of diseases using speech recognition. After taking into account the patients’ histories and circumstances, Babylon offers appropriate courses of action. The app also reminds patients to take their medication and finds out how they’re feeling.

    Molly was the first virtual nurse developed by Sense.ly, a medical start-up. It aims to help people to monitor their condition and treatment. Nurses use machine learning to support patients with chronic conditions in-between doctor’s visits. Customised monitoring and follow-up care is part of its service too, with a strong focus on chronic diseases.

    The AiCure app maintained by The National Institutes of Health monitors patients’ medication compliance. It uses a smartphone’s webcam and AI to confirm patients’ medication ingestion and helps them to manage their conditions.

    AI still has a long way to go. It will be exciting to see how Africa’s health systems adopt it in their eHealth strategies and use it to transform health and healthcare.