Increasing demand for AI tools in the medical field, growing focus on reducing the workload of radiologists, influx of large and complex datasets, growth in funding for AI-based start-ups, and the growing number of cross-industry partnerships and collaborations.
Pandemics such as COVID-19 require investments for additional manpower, equipment, consumables, and other resources to ensure 100% preparedness for safety in hospitals and, if needed, eventual treatment of patients.
AI and radiomics, which are being applied to X-ray and CT scans as advanced imaging tools in the detection and follow-up of COVID-19, have been able to diagnose the presence of the infection.
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The Massachusetts General Hospital (Boston, US) also used AI-powered robots, which obtain vital signs and provide care to COVID-19 patients. This allows healthcare staff to potentially reduce human contact, which is important to curb the spread of the disease.
The growing awareness of the benefits offered by AI techniques and their wide application areas has led to the increased adoption of these products and services in the healthcare market.
Currently, many healthcare professionals have doubts about the capabilities of AI solutions in terms of accurately diagnosing patient conditions. Considering this, it is challenging to convince providers that AI-based solutions are cost-effective, efficient, and safe solutions that offer convenience to doctors as well as better care for patients.
According to the National Institute of Health and Family Welfare (NIHFW), in India, it is estimated that there are 2 to 2.5 million cancer patients at any given point of time; every year, about 0.7 million new cases are reported in the country. Furthermore, the healthcare sectors in emerging economies, such as India and China, are rapidly growing, resulting in the introduction of new medical technologies with a special focus on advanced imaging equipment.
Key Market Players
Some of the prominent players operating in the AI in medical diagnostics market are Microsoft Corporation (US), NVIDIA (US), IBM (US), Intel (US), Siemens Healthineers (Germany), GE Healthcare (US), Digital Diagnostics (US), Xilinx (US), InformAI (US), Enlitic (US), Day Zero Diagnostics (US), Aidence (Netherlands), Butterfly Network, Inc. (US), Prognos (US), Zebra Medical Vision (Israel), Viz.ai (US), Quibin (Spain), Qure.ai (India), Therapixel (France), and HeartFlow (US).
But NLP technology is now advancing at a breathtaking rate, opening up new possibilities for conversational AI.
AI will augment the work of human clinicians in hospitals in various ways.As one example, Gauss Surgical uses computer vision to monitor blood loss during childbirth.
The vision of precision medicine is more ambitious, the technical challenges more complex, and the potential impact greater than perhaps any other application discussed here.In a nutshell, the field of precision medicine aspires to create treatments that are individualized for each patient based on his or her particular genetic, environmental and behavioral context.Precision medicine is not a new concept, but the advent of “big data” (especially genetic data) and modern machine learning have brought its full realization within reach.
Several trillion gigabytes of health data will be generated this year, a figure that would have been unimaginable a few short years ago.The premise of precision medicine is that if a computational system knows your entire genome, your metabolic profile, your microbiome composition, what foods you eat, how often you exercise, how much you sleep, and a thousand other data points about you; and it also understands a disease’s particular pathway in your body down to the molecular level; then it can synthesize all this information and craft a pharmaceutical and/or behavioral regimen specifically tailored to optimize your body's response.No human could ever perform such herculean data crunching and latent pattern recognition.
The company is focused on cancer treatment, although it has recently devoted resources to the fight against COVID-19.Other well-funded companies in this category include Syapse and GNS Healthcare.Precision medicine has for decades stood as a tantalizing but unfulfilled possibility.
AI can play a key role here.Provider OperationsEvery time a patient interacts with a healthcare provider, dozens of support processes take place in the background: patient check-in, benefit and verification discovery, claims processing, invoicing, prescription orders, supply chain management, and more.
Chatbots are used for various applications in healthcare industry.
Patient data contain personal, private, or confidential information and requires strict safeguards to prevent its misuse.
This is a very difficult task as most AI-based platforms are consolidated and require extensive computing power owing to which patient data, or part of it, can be required to reside in a vendor’s data set.Growing usage of healthcare chatbots by clinicians and hospitals to increase their patient engagement rate, are driving the growth of the healtchcare chatbots market.PatientsPatients are the largest end users of healthcare chatbots.
Healthcare chatbots interact with potential patients visiting a site, provide a possible diagnosis, help find specialists, schedule appointments, and improve access to the right treatments.
Healthcare providers need to identify diseases and analyze a large amount of healthcare information to make critical decisions.
In an effort to improve the quality of care and reduce costs, healthcare providers are increasingly turning to IT-enabled strategies and software for the appropriate identification of diseases and better treatment alternatives.
