• AI
  • Robots could be good for your health

    In his book The Rise of the Robots, published by One World, Martin Ford proposes social and economic scenarios for robots that are good for output, but not so good for people. He sees significant upheaval and displacement from employment across a wide range of commercial and industrial activities and across middle and low income families. The drop in income, so spending power, will degrade economies.

    Simultaneously, robots aren’t paid and don’t spend money. He sees this as exacerbating the social and economic impact.

    Healthcare’s the activity that’s different. He sees the robots marching into healthcare that’s already over-stretched as needs and demands continuously outstrip supply. Four roles are crucial:

    Artificial intelligence in medicineHospital and pharmacy roboticsRobots that care for the elderlyUnleashing the power of data. 

    For low and middle income countries and health systems, sustained investment in robots could be part of the solution. They can improve healthcare professionals’ productivity and help to meet demand.

    They should find a place in Africa’s eHealth strategies. Small scale investment will lay out a trajectory for the future.

  • AI helps to predict cancers’ trajectories

    Many years ago, people in the UK referred to cancers as “a growth.” While it might have lacked scientific precision, it encapsulated cancers’ changing characteristics. The country’s Institute of Cancer Research (ICR) at London’s Royal Marsden Hospital, and part of University College London (UCL), says tumours’ constantly changing nature’s one of the biggest challenges in treating cancer, especially when they evolve into drug-resistant forms.

    It reports that its ICR scientists, working with colleagues at Edinburgh University have used AI to identify patterns in DNA mutations in cancers. The information can forecast future genetic changes to predict how cancers will progress and evolve. The technique, Repeated Evolution of Cancer (REVOLVER), predicts cancers’ next moves so doctors can monitor tumour’s progress and design the most effective treatment for each patient. 

    Three organisations financed the research, published in Nature Methods. They were the Wellcome Trust, the European Research Council and Cancer Research UK. Their support for REVOLVER’s created what’s seen as a powerful AI tool. It’s revealed previously hidden mutation patterns located in complex data sets.

    Teams from ICR and the University of Edinburgh working with colleagues from the  Birmingham University, Stanford University and  Queen Mary Universities London found a link between some sequences of repeated tumour mutations and survival outcomes. It suggests that repeating patterns of DNA mutations could be prognoses indicators. This can help to specify future treatment.

    AI success stories provide material to consider in Africa’s new eHealth strategies, to support leading specialist hospitals to set up a wide range of AI initiatives. They could focus on Africa’s current and emerging health and healthcare priorities.

  • Will AI and Blockchain converge to enhance health analytics?

    While AI and Blockchain are seen by some to offer powerful tools, a view’s emerging that combining them offers significantly more potential for Big Data and health analytics. Or, is it just another dose of eHealth hype? An article in Health IT Analytics  says in the US, AI and Blockchain are now tools of choice for developers, providers and payers in improving their eHealth infrastructure.

    But, it acknowledges that both are near their hype curves peaks. Some providers and payers are reluctant to invest heavily at their maturity stages. Concerns over security, utility and Return on Investment (ROI) are justifications for some organisations to defer investment, leaving others to provide evidence that combining AI and Blockchain can succeed in secure the large data sets and exchanges that Big Data needs for innovative analytics.

    Access to data’s one obstacle. Most data resources are held securely and privately by several institutions. Opening them can create cybersecurity vulnerabilities. Despite this, ideas are fermenting of using Blockchain to produce metadata about the datasets available at several organisations. It can also provide secure, peer-to-peer data exchange. Blockchain can be a pointer to where full data sets are stored, allowing for discoverability without requiring data sets to move each time a transactions completed.

    This strategy enables organisations to keep sensitive data, such as Protected Health Information (PHI) and Personally Identifiable Information (PII) off Blockchain. It’ll reduce risks of breaches. Instead, minimal but sufficient data should be held in Blockchain.

    These comprise complex decisions and projects. It seems premature for Africa’s health systems to pursue combined AI and Blockchain strategies in the medium term. There are other eHealth priorities to address, such as using mHealth to support remote health workers with access to test results and improving their co-ordination with colleagues.

    If the AI and Blockchain are converging in healthcare, Africa’s health systems can watch trajectories and learn from them. If they deliver a significant proportion of their potential, a challenge for Africa’s health systems may be to avoid a sudden disruption to their eHealth strategies and plans. While this can be costly, missing new eHealth opportunities has a cost too, often of missed benefits. 

  • AI passes a stiff test at London’s Moorfields Eye Hospital

    England’s Grand National run at Aintree is gruelling. It has 30 fences, two with open ditches, in a distance of 2.25 miles that’s completed twice. AI has just moved up the field in the eHealth equivalent. 

    An AI project at London’s Moorfields Eye Hospital with Google’s DeepMind has accurately diagnosed eye conditions from scans. As ophthalmologists’ workloads and their complexities increase, diagnostic imaging is expanding faster than specialists can interpret the results. AI already has a constructive reputation in classifying two-dimensional photographs of some common diseases it’s reached the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans. 

    At Moorfields, a novel, deep learning architecture is now applied to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients. The research found that after training on 14,884 scans, AI’s referral recommendations of sight-threatening retinal diseases reached, and sometimes exceeded that of experts. 

    Other benefits include:

    Tissue segmentations produced by the architecture are device-independent representationsReferral accuracy’s maintained when using tissue segmentations from a different devicePrevious barriers to wider clinical use without prohibitive training data requirements across several pathologies have been removed.

    After training, the algorithm assigned diagnoses to 1,000 patients’ scans whose clinical outcomes were already known. The same scans were shown to eight clinicians. Four were leading ophthalmologists, four were optometrists. They classified the diagnoses into four referral types,  urgent, semi-urgent, routine and observation. AI performed as well as two of the world's leading retina specialists. The error rate was 5.5%. More strikingly within this performance, the algorithm didn’t miss any urgent cases.

    The impact of the project’s global. For Africa’s health systems, the challenge’s entering the AI Grand National and making sure they don’t fall at any of the daunting fences. It offers an eHealth strategic scenario that extends what is now relatively conventional EHRs and mHealth. AI can extract more value from them than originally imagined.

  • AI needs faster data access for researchers and analysts

    Maximising AI’s potential for clinical research and breakthroughs needs access to large data volumes to train then deploy AI models. A white paper by International Data Corporation (IDC), sponsored by: Pure Storage, says Hard Disk Drives (HDD) are too slow for the task. It says All-Flash Arrays (AFAs) are faster and more accurate. 

    An AFA’s a Solid State Disks (SSD) storage system with several flash memory drives. Instead of searching for data on spinning HDDs, SSDs have no moving parts, so are faster to access. The Tech Republic has an entry-level guide on AFAs. It says they’re disrupting traditional data storage resources. 

    IDC’s white paper emphasises AI as a learning process where researchers and analysts need prompt access to data for clinical projects. It has two main benefits:

    Shortens the clinical innovation time from desk to bedsideAttracting and retaining scarce clinical researchers and data scientists who look for leading-edge AI investment and infrastructure to succeed.

    Improved data response times with AFA benefits clinical teams that need access to clinical data for direct patient care too. Faster response times help to improve their productivity and efficiency. They also help to minimise eHealth frustrations and improve job satisfaction. 

    As eHealth foundations are vital parts of eHealth strategies, Africa’s health systems should consider SSDs along with expanding network capacity and connectivity capacity.  

  • Stethee reinvents the stethoscope with AI

    The worlds first Al enabled stethoscope system has been launched by M3DICINE Inc.

    The design itself is revolutionary and operates as easily as the traditional stethoscope. However, it allows users to listen to the lung and heart sounds with a more sophisticated amplification and filtering technology. Heart and respiratory sounds captured are sent via Bluetooth to the Stethee Android or iOS App which enables a wider range of diagnostic capabilities.

    The Stethee system comes in three core products:

    FDA cleared Stethee Pro for medical and healthcare professionalsStethee Vet for veterinarians and animal professionalsStethee Edu developed specifically as an education and research tool

    The technology platform behind the Stethee AI engine , named “Aida” can analyze the heart and lung sounds to build a unique personal biometric signature.  In addition to this, Aida automatically tags geo-location and environment data to each sample in real time.  This offers a completely new dimension of data analytics for public health planning by allowing one to understand what effects environmental factors such as pollution, temperature or humidity have on our heart and lungs.

    Aida also analyzes this encrypted and anonymised data in order to learn and report back quantitative clinically actionable data to vets, doctors and other healthcare professionals. Not only does it identify and analyze heart sounds and respiratory activity but also patterns that may indicate a disease condition. The data is represented in real time in the Stethee App, therefore making it easy to understand vital signs.

    The potential for the Stethee to be used in remote rural areas is quite vast because its relatively easy to use and results can be shared and analyzed promptly by a medical specialist anywhere in the world. This is invaluable to the improvement of patient care, more especially to remote rural areas where access to screening services or a cardiologist is very difficult.

  • England’s NHS tale of two technologies

    While some of England’s NHS is leading the way with AI, as previously reported in eHNA, other parts seem heavily reliant on FAX machines. This tale of two technologies, with apologies to Charles Dickens for modifying his book’s title, was revealed in a survey by the Royal College of Surgeons (RCS).

    The Independent, a newspaper, says the RCS finding’s that the NHS remains "stubbornly attached" to fax machines. It identified almost 9,000 FAX machines in about 75% of NHS trusts. Using this archaic technology for a significant proportion of their communications looks even more odd when at the other extreme, aspirations for AI are underway and being fulfilled.

    The BBC has highlighted that one NHS trust has 603 machines, over 6.5% of the total identified. Nest in line are 400 and 369 at England's biggest trust. Taken together, the three organisations have about 15% of the total. It’s a heavily skewed distribution.

    In its blog last year, Deep Mind had identified that the NHS was the world’s biggest buyer of FAX machines. The RCS findings are not a surprise. 

    Another dimension of the ICT legacy is that National Health Executive, a blog, highlighted that most NHS trusts had about 160 different computer systems. It seem that one end of the NHS’s two technology continuum’s a long way from the other, AI end. It reveals a legacy and investment challenge that all healthcare organisations face.

  • AI in England’s NHS improves precision and saves time

    The UK’s NHS is 70 years old this year. It was born on 5 July 1948, so happy birthday Since then, it’s seen a continuous surge in new technologies and techniques leading to transformation. eHealth’s been an increasing component over the last 30 years or so. Now, AI’s coming into the investment frame.

    The Guardian has an article on AI at  Addenbooke’s Hospital, Cambridge. It’s used to delineate tumours. AI completes the work in minutes. Doctors use InnerEye from Microsof to mark-up scans prostate cancer patients. Images from completed scans are anonymised, encrypted and sent to InnerEye to create 3D models. It’s learnt to do by training with previous patients

    Brain tumours are next on the list. For some cancer patients, may have to review over 100 images doctors to plan their radiotherapy treatment. It’s obviously time-consuming, both for doctors, their colleagues and can defer the start of treatment for patients.

    The other significant AI benefit’s greater precision, so improved effectiveness. By focusing more precisely on cancer cells, it helps doctors providing radiotherapy treatments to avoid healthy tissues. 

    Both benefits, precision and time-saving, are leading to more streamlined, more effective and more efficient healthcare. These are some of eHealth’s main goals.

    The NHS has also Heart Flow, developed by Stanford University. It’s AI uses routine CT scans from patients with suspected heart disease AI to create personalised 3D models of their hearts and blood flow. It reveals how specific blockages disrupt blood flow in individual blood vessels, leading to better treatment decision, or none where appropriate. Over half the patients with HeartFlow data avoided angiograms. 

    London’s Royal Free Hospital has an AI development underway. It analyses and refines blood test results and to predict which patients are most likely to die, or have serious problems such as kidney failure. It’s trained from almost 1 billion blood test results from 20 hospitals, and identifies subtle changes in red and white blood cells and electrolytes such as sodium and potassium. It reveals which patients’ health may be deteriorating. 

    Other AI services include:

    Skin cancer diagnosesEye disorders from retinal scansHeart disease from echocardiogramsStrokes.

    The results are encouraging. Africa’s eHealth strategies and plans need a place for AI the data it needs.

  • What does eHealth have to do for radiology services?

    Radiologists are in short supply.  Radiology workloads and demand are rising. A report from Digital Health explores the opportunities to use AI and Radiology Information Systems (RIS) in the UK’s NHS to fill the gap. It identifies essential requirements for national eHealth too.

    Two solutions are proposed, both needing RIS: 

    Sharing reporting workloads across healthcare organisations

    Using AI to automate some of the clinical workload.

    Current images and workflow sharing relies on  an Image Exchange Portal run by Sectra. It’s fast, but seems it needs replacing to meet radiologists’ needs of: 

    Knowing when an image is there for reviewA single system that displays their own images and other clinicians’ images for individual patientsAccess to each patient’s reporting history and images needed for full and useful reports.

    This needs a specific organisational structure, a lesson for Africa’s health systems. In the days before England’s National Programme for IT (NPfIT) was abandoned, radiology information could be shared across each of England’s five NPfIT regions.

    Since then, smaller geographic consortia have emerged to procure Picture Archiving and Communications Systems (PACS) and RIS from single vendors. It achieves lower costs, smoother, more efficient workflows and makes their sharing easier. Patients, radiologists and organisations outside these consortia don’t benefit.

    Vendor-neutral standards are the solution. Two, Soliton and Wellbeing Software, provide solutions share radiology reporting across several sites with different RIS vendors. Their impacts are constrained because there isn’t a single or unified procurement organisation.

    Is RIS becoming obsolete? EPRs and PACS may be able to deal with scheduling and remote reporting. Some radiologists see it differently. They may be increasingly dependent on RIS.

    AI may be a solution too. It’s already dealing with some basic reporting. Wellbeing has a platform for  an AI algorithm to report directly into its RIS. 

    Agfa uses the term Augmented Imaging (AI). It’s exploring the potential for its AI to automate some administrative tasks. Algorithms are already available to detect TB on chest X-rays. Partnering’s already in place with hospitals and research institutes that need Agfa’s workflow engine to develop their own algorithms. 

    Lessons for Africa’s eHealth are clear. Radiology needs its own eHealth engagement, strategy, plans and procurement.

  • AI and machine learning need data storage resources

    Many things come in bundles. Amit Ray, author of Mindfulness Meditation for Corporate Leadership and Management says “As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership.” It’s not enough. A report by Source Media, sponsored by Pure Storage says powerful, advanced computing and storage capacity and capabilities are needed too.

    It recognises AI’s “vast” potential. Currently, some radiology departments use it effectively to improve workloads. Progress across other clinical activities depends on extra computing and storage power for two activities, training and clinical use.

    When researchers deliver AI and machine learning techniques to clinical practice and healthcare, solutions need huge amounts of data for training models, including labelling data. It’s especially important for neural networks. These are hardware and software patterned on the way neurons work in human brains. They’re deep learning technologies often focusing on solving complex signal processing or pattern recognition problems.

    If storage’s inadequate, it can’t keep up with the workload. The result’s diminished AI. Healthcare’s typical eHealth investment model’s to buy enough computing storage infrastructure as a minimum requirement, then expand it a few years after it’s clogged up. Eventually, it’s replaced with modern solutions after a period of obsolescence.

    This doesn’t fit AI and machine learning. It has to match the computer power and storage capacity needed as AI and machine learning expands. Developers and healthcare organisations can then move beyond exploring AI’s potential and bring into full use. The, patients benefit.

    While assembling the resources needed for AI and machine learning’s challenging for Africa’ health systems, the infrastructure requirements add to the constraints. Before venturing into the AI space, it’s essential to contemplate and deal with the whole resource requirements and their affordability.