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The growth in artificial intelligence (AI) and allied deep learning, predictive analytics and digital twin concepts continue to disrupt technology. In the disruption, the healthcare industry is not left far behind with analysts and consultants collaborating together to investigate the implications of each new tech development on patients, providers and ancillary industries.
Deep learning, which trains machines to think like the human brain, has a great potential to reduce the turnaround time during the drug discovery process through predictive modeling. By supporting better clinical decisions through electronic health record (EHR) interoperability and analytics to improve medical imaging and diagnosis processes, predictive analytics leverages insights from historical healthcare data, machine learning and AI to design a personalized care regimen, support a diagnosis, reinforce large-scale population health initiatives or manage chronic diseases. This has a twin benefit as it enables patients to benefit from medication adherence and behaviour modification techniques improving outcomes, making hospitals register reductions in readmissions, infections, adverse events and mortality rates.
A digital twin is a virtual environment leveraging the growth of IoT, big data and machine learning in health care. These technologies facilitate data-driven and scenario-based decision-making encouraging all stakeholders to work together to find solutions for common problems. This is a great boom to hospitals as they can deploy a digital twin to reduce wait times, improve inventory management and safety, and minimize billed hours and average length of stay.
GE Healthcare is augmenting its capabilities in medical imaging through its portfolio of high-end radiology ultrasound systems integrating cloud connectivity, AI technology and advanced algorithms, with its partnerships with Intel and Nvidia to develop its deep learning platform to apply AI to medical imaging
GE is a front-runner into digital twin technology, modeling virtual hospital settings using its healthcare-specific simulation platform. GE has collaborated with the Johns Hopkins Medicine for an advanced Capacity Command Center that applies analytics and simulation for better decision-making in its Baltimore medical facility. By working on digital twins of patient pathways, the hospital is able to predict patient activity and strategize capacity planning according to demand. With the application of digital twin, the hospital has been able to demonstrate significant improvements into patient experience and volume, patient safety, and in the movement of patients in and out of the hospital better their access to life-saving treatments and emergency care.
GE Healthcare is setting up examples by honing its capabilities in applied intelligence deploying a predictive analytics platform to transform large, disparate patient data into intuitive and actionable intelligence. The aim is to help customers, coordinate, manage and benchmark patient care at a population level.
Alphabet is on a mission to democratize medical information, a current challenge in health care. Alphabet through its subsidiaries is actively investing in technology upgradations for actionable use of healthcare data and is additionally expanding its capabilities in medical imaging. Alphabet is focussing on predictive software that deploys medical record data (e.g., patient demographics, lab results and vital signs, and previous diagnoses and procedures to predict outcomes including patient mortality. The most promising development is the AI software developed by Verily and Google that can predict the risk of cardiovascular disease by analysing retinal fundus images to identify risk factors such as blood pressure and age, smoking habits to assess the likelihood of a heart attack or other cardiac event. The predictive algorithm developed by Verily and Google generates attention maps that are less invasive and more cost-effective than current tests including the coronary calcium CT scan, leading to new frontiers in preventative care.
IBM is working close with giants leveraging its AI platform Watson to enable the integration of AI into social program management, drug discovery, personalized care delivery and genomics using automated workflows and individualized care plans. IBM has entered into several partnerships in this regard with leading research institutions, universities, hospitals and pharmaceutical companies.
IBM's in partnership with Pfizer to develop a cloud-based cognitive tool with natural language processing, cognitive reasoning technologies and machine learning is making great strides in immune-oncology and neurological disease research. Watson assists Pfizer stratify patients to work on combination therapies and develop new drug targets for personalized care delivery. IBM and Pfizer have collaborated for Project BlueSky, which uses analytics and sensors to continuously collect and monitor clinical data from patients with Parkinson's disease. The data collected will provide a real-time estimate of a patient's motor function and is more efficient than the traditional episodic assessment where patients had to visit a hospital during clinical trials. This AI-enabled drug discovery process will speed up the clinical trial process since phase 3 trials easily can be scaled up to cover several hundred patients.
IBM Watson Health scales up to develop capabilities into medical imaging, population health, cloud-based health care intelligence and genomics. In this regard, it has made several acquisitions including Explorys, Merge Healthcare, Truven Analytics and Phytel in a bid to strengthen its value-based care portfolio. IBM's expertise in this regard will aid to identify genetic reasons for diseases and deploy that valuable information to design pathways for personalized therapy.
Market estimates ascertain that AI and associated technologies will become more imperative and shift its focus toward personalized care. Recent research estimates point that the Personalised Care medical industry will be worth $6.6 billion by 2021. AI has the potential to cut down health care costs by 50% and additionally improve patient outcomes by more than 50% by cognitive automation, improving workflows and increasing accuracy.
