Artificial Intelligence (AI) in Healthcare: Market Size, Trends and Analysis

Technology today is all-pervasive and the #healthcareindustry is no exception. With the advent of #artificialintelligence (AI) and related technologies, healthcare is undergoing an important transformation. These technologies have applications across various fronts including data management, disease management, patient management, diagnosis and care, and target identification.

Artificial Intelligence (AI) refers to an array of cognitive technologies that include, but are not limited to machine learning, natural language processing, computer vision and context-aware computing. The goal of AI systems is to develop systems capable of tackling complex problems in ways similar to human logic and reasoning. These systems are designed to save reducing operational and administrative expenses and are crucial in under-served areas in healthcare where such technology will prove lifesaving.

According to Global Data, the Artificial Intelligence in Healthcare market is set to cross a valuation of US$ 52 billion by 2027, growing at an aggressive CAGR of 39%.

Strategic Partnerships Will Accelerate the Growth of the AI in Healthcare Market

International blue-chip technology companies such as GE Healthcare, Siemens Healthcare, and Roche are investing heavily in product development and partnership to enhance their AI capabilities for the treatment of various diseases. IBM’s Watson health is a platform that brings together advanced #technologysolutions including options for AI, #blockchain, and #dataanalytics to provide healthcare solutions. The AI industry is witnessing a number of strategic partnerships to provide innovative AI solutions. In August 2021, GE Healthcare entered a partnership with Amazon Web Services (AWS) to provide cloud-based and AI imaging solutions to healthcare providers and hospitals. Similarly, GE Healthcare and Lunit partnered to develop AI algorithms from Lunit INSIGHT CXR accessible through GE Healthcare’s Thoracic Care Suite, to assist #radiologists and enhance patient outcomes.

“AI holds enormous promise for the future of medicine, and we’re actively developing a new regulatory framework to promote innovation in this space and support the use of AI-based technologies.”

-?????????Dr. Scott Gottlieb, FDA Commissioner

AI in Healthcare Research & Development

AI helps aggregate and synthesize information by which potential #newdrugs or #medicines can be discovered, and then individual compounds, which are more likely to succeed in #clinicaltrials trials are shortlisted. Novel drug candidates are generated with help of machine learning techniques?

“Harnessing the power of modern supercomputers and machine learning will enable us to develop medicines more quickly, and at a reduced cost.”

-?????????John Baldoni, Head of new drug discovery unit, GSK (Brentford, UK)

Resistance to Apply AI Techniques Poses Restraint

Lack of data that identifies or supports healthcare decisions, ambiguity with respect to law and technical know-hows, and the high costs are the main factors that result in reluctance among users to adopt or upgrade to new technology in healthcare organizations. In addition to this, users are constantly worried by the increasing security and privacy issues, such as access rights to data, storage, security while transferring data and data breaches that come with adoption of big data technology.

The Influence of Machine Learning, Neural Networks and Deep Learning in Healthcare

Machine learning (ML) involves building models that use of data and algorithms to imitate the human learning processes. ML can broadly be divided into two categories. In the first, ML enhances data-driven decision-making, often in combination with business intelligence (BI) and data analysis tools. In the second, ML is used to build models and incorporate AI into larger applications. This includes providing machine learning as a service (MLaaS) and using developer tools such as application programming interfaces (APIs) to facilitate adoption. Machine learning can be used in the #healthcareindustry for precision medicine – predicting what treatment regime works best for a patient, especially proven efficient in the oncology, via supervised learning techniques. Digitalizing #patientdata has made machine learning feasible and has applications ranging from early detection to #treatment evaluation across different phases of patient life cycle.

Deep learning is a subset of machine learning that has gained significant attention over the last few years due to its ability to solve hard and large-scale problems in areas such as speech recognition, natural language processing and image classification. Convolutional neural networks (CNNs), a type of deep learning technology, is especially useful in image analyses, and can process data from MRIs or x-rays.?

AI has caught the attention of the top leadership of many companies, who have started to see the benefits of incorporating different emerging technologies across the admin, IT and clinical teams of their organizations. With the plethora of valuable data in constant generation, artificial intelligence technology is set to become indispensable in the #healthcaremarkets in the years to come.?

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