4TH QUARTER 2018
Q: Is health tech adoption largely being driven by consumers? If so, how? Yes, it seems like health tech adoption is largely being driven by consumers, especially in the wearable healthcare space. Although Smart technology related healthcare start-ups across the globe are attracting significant investment, there still seems to be a low AI adoption in the Healthcare sector in general when compared to other industries such as Telecommunication, High technology, Automotive and Financial services. Some of the characteristics of early AI adopters include companies that are digitally mature, typically larger businesses, adopting AI and other smart technologies in core activities, focusing on growth over strategies and having C-level support for AI. Some examples of value that can be created across the Healthcare value chain includes: • Predict disease, identify high-risk patient groups, and launch prevention therapies • Automate and optimize hospital operations; automate diagnostic tests and make them faster and more accurate • Predict cost more accurately, focus on patients’ risk reduction • Adapt therapies and drug formulations to patients, use virtual agents to help patients navigate their hospital journey
SYNAPSE
30
Q: These new technologies will require new skills outside of medical qualifications. What are the future skills that will shape the industry in the near future? Some of the new skills outside the medical qualifications does not only include technical skills such as those from data scientists, data translates, data engineers, AI and machine learning engineers, IoT specialists, but also psychology and skills related to emotional intelligence and communication. For students, a future-focused curriculum is a necessity. The World Economic Forum identified 16 skills that are needed in the 21st century—including creativity, collaboration, initiative, and adaptability—but are not included in standard curricula. People are also questioning the “learn then earn” model, wondering if lengthy degree programs still make sense in a world of fast-changing jobs. Instead, there are calls for a new deal on lifelong learning. The implication of these changes is clear: companies need to update the skills in their workforces, and individuals need to acquire skills that work with, not compete against, machines. For people already in the workforce, reskilling will be essential. Much of this reskilling can occur on the job through stronger professional development programs. For people transitioning between jobs, vocational and adult education programs must be strengthened. These work best when they are short, affordable, and closely linked to the job market. Nanodegree programs are one recent innovation in this space. Before the medical profession can realize the full potential of AI, health care providers must adopt significant changes in the way they do business, commit to a substantial investment in computing power and technical expertise, and work to increase the availability of the fuel that will power progress: data, including medical records. Q: After AI, what do you predict will be the next big health tech innovation? The next big health technology innovation will be a fusion of smart technologies that includes AI, biotech, neurotech, nanotech, virtual and augmented reality, blockchain, IoT, 3D printing, robotics, drones, quantum computing, etc. With all the investment and talent focusing on AI, one can also expect significant enhancements and breakthroughs in AI technology that would lead to more value adding applications. Some of the new exciting AI Applications in Biomedical Engineering includes: • Computational Pathology/Tumour detection: AI plays a major role
in lesion identification and classification, serving as a good prediagnosis tool in precision cancer care. Some of the tasks within this scope are: • Tumour Identification: This is a classification tasks that assigns the identified area into one of many output classes.It can also be a two-class problem where the task is to say if the tumour is present or not present (tumour detection). • Segmentation / Tracking: Identify areas of tumour or any other anatomy of interest in images of any modality - MRI, pathology slides, Ultrasound, etc. (U-Nets) • Registration - Multi-modal registration of images using Convolutional Neural Networks. Especially for non rigid registration • Touchless interaction in OR (Operating Room) - Machine Learning approaches are used to solve pattern recognition problems of capturing motions and gestures and using them to interact with modalities in the operating room. • Navigation in Image Guided Surgeries: Endoscopy is one example where machine learning approaches are used to identify the pose and scene and relate it to the point in surgical workflow. This can be used to predict surgery times and other aspects of the remaining surgical workflow. • Health-care robotics (Assisted surgery, haptic rehabilitation systems, laboratory automation systems) • Drug discovery • Protein Folding • Sequence Analysis (gene finding, multiple dataset integration) • Brain-Computer Interface and Neuroprosthetics • Experts Systems for advising health-care professionals • Epidemiological Data inference (e.g. tracking epidemics, finding patterns of exposure/symptoms) Q: What can delegates expect from your talk at the AI Africa Expo. As Cortex Logic is an AI Engine for Business, we are wellpoised to operationalize AI and help platform businesses and large enterprises thrive in the Smart Technology Era. AI Expo Africa is also a great opportunity to see how AI is now impacting many aspects of Commerce and Enterprise in Africa, and delegates will gain great value from attending. I’ll be sharing some practical applications of AI technology in various industries. • Get good perspective on operationalizing Big Data & Analytics, IoT and AI and its benefits • Discover how end-to-end AI solutions can unlock value from all available structured and unstructured data to: • increase operational efficiency, effectiveness and revenue • create strategic value via faster, better and more proactive decisions, enhanced scalability, new business models, and revenue growth opportunities • enhance customer experience via real-time, on demand, digital, personalized service delivery, assistance and advice which is enabled via 360 degree insights about the customer • enable more targeted sales and marketing • Get information on some of these solutions include strategic business transformation & optimization, human capital valuation & employee profiling, intelligent virtual assistants, robo-advisors, process optimization, predictive maintenance, fraud detection, churn prediction, advanced risk scoring, machine learning-based trading, real-time customer insights, smart recommendations and purchase prediction, personalized search, cyber security, medical risk prediction, and precision medicine. • Working collaboratively with innovate AI partners that can help business thrive in the Smart Technology Era and be agile, innovative and adapt quickly to stay relevant given the swift pace of change and disruption to business and society. ai Excerpts from this interview appeared as a radio interview on eHealth.