If you follow Artificial Intelligence and Machine Learning, you've probably heard a lot about the applications of these emerging technologies in the healthcare space. This entire hype train has stemmed from the raw processing power that machine learning models are able to employ. Recently, Machine Learning models have been utilized and leveraged to generate and build statistical models from medical data. These models have had various use cases, ex. AI has been used to assist radiologists in performing diagnoses using Convolutional Neural Networks. Despite this, there has been an evident hesitation to exploit medical data to develop efficient and effective machine learning models.

Matthew is Senior Editor at Emerj, focused on enterprise AI use-cases and trends. He previously served as podcast producer with CrossBorder Solutions, a venture-back AI-enabled tax solutions firm. Prior, Matthew served three years at the World Policy Institute as a news editor and podcast producer. Healthcare is an increasingly complex sector of the global economy, and AI is playing an active role in the worldwide evolution of the industry throughout its many disciplines. In a Deloitte study released earlier this year, 85% of respondents among healthcare business leaders said their enterprise was increasing AI spend before 2023.

Many of the causes of healthcare worker attrition are related to the stressful nature of the healthcare work industry. Many employees work long hours and often experience high burnout. Employee attrition in healthcare is an issue because it exacerbates the issue of the limited supply of workers in the space. Since the healthcare space is significantly understaffed and many healthcare employees are overworked, the quality of care and the speed to care are often negatively impacted. In general, efforts towards reducing healthcare attrition involve improving the quality of the work environment.

For many years, AI has held science fiction-like promise. As on today, almost every field, domain and industry has been impacted as well as transformed by AI when it comes to processing efficiency and improvement. However, Artificial Intelligence and Machine Learning hold considerable promise for the field of Healthcare. Right from hospital care, data entry, in-patient, and out-patient assistance, and insurance, AI applications are revolutionizing how the industry works as a whole to cut down on costs and significantly improve patient outcomes. A study by Accenture has predicted that growth in the AI healthcare space is expected to touch $6.6 billion by 2021 with a CAGR of 40%.

This week, Nvidia, a company best known for its high-powered graphic processing units (GPUs) debuted a platform for the development of AI-powered medical devices. The device, called Clara Holoscan MGX, provides computing power allowing medical sensors to process multiple data streams in parallel, train AI algorithms, and visualize biology in real time. Clara Holoscan MGX, debuted at the Nvidia's 2022 GTC conference, is an "open, scalable robotics platform," as CEO Jensen Huang put it in a keynote address. It's a hardware and software stack designed to help connect robotic medical devices or sensors, with AI applications. Take the process of endoscopy as an example.

"Microsoft's cloud boss says the company doesn't want to compete with doctors." This statement is coming as Microsoft and other Cloud providers are making their way into the healthcare space. Microsoft recently announced the acquisition of a speech recognition company that doctors use to keep notes on meetings with patients. The move will provide Microsoft an expanded Cloud offering and, perhaps incidentally, access to lots and lots of data when it comes to healthcare and diagnosis. Like any knowledge-based industry, doctors should be wary of the long-term impacts of artificial intelligence on their industry.

The COVID-19 pandemic is continuing to disrupt businesses across the world. Being one of the most hustling sectors globally, healthcare has turned into the most reshaped ecosystem amid the crisis. In the initial phase of the pandemic, the healthcare ecosystem was afflicting several challenges in terms of delivery of services. However, technological innovation enforced the sector to adopt digital models, resulting in the holistic development of the industry. Reimagining the healthcare space through digital innovation creates an ecosystem for improved delivery of healthcare services.

The healthcare space is growing by leaps and bounds. According to a report, global healthcare expenditure is expected to reach USD 10 trillion by 2022. Owing to multiple factors like technological advancements, expensive infrastructure, growing health-related awareness, and a rise in chronic health conditions, the healthcare market is evolving faster than ever. With time, the use of technology has brought structural changes to the healthcare industry, for the better. Whether it's managing endless administrative processes in hospitals, providing personalized care and treatment, or facilitating better access, technological advancements like mobile healthcare, also known as mHealth, and machine learning in healthcare have streamlined the healthcare sector to a great extent.

TECHNOLOGIES such as artificial intelligence (AI), machine learning (ML), the internet of things (IoT), 5G, and robotics have plenty of potential in the healthcare space. However, most healthcare organizations are unable to leverage those technologies because they neither have the digital infrastructure nor the technology talent to turn their vision into real projects. "Health services have a lot to do before they can make the most of these opportunities," Waitematā District Health Board (DHB) Clinical Director Innovation Robyn Whittaker told Tech Wire Asia underscoring the fact that industry professionals aren't oblivious to the power that technology holds. Whittaker and her team at Waitematā DHB have just made it to the list of finalists in the talent accelerator category at the IDC Digital Transformation Awards. To prepare for the digital age, Whittaker's team is working on the foundational layers, digitizing data, and making it available for clinicians and researchers looking to make sense of that data.

As electronic health records have become acceptable and accessible, there is so much in the healthcare space that can transform with machine intelligence. Machine learning and artificial intelligence technologies are now being used by healthcare institutions to improve diagnosis and care. But, to facilitate the technology's widespread use and implementation, it will have to pass the litmus test of compliances and standards. While that looks less likely to happen with AI implementation anytime soon, another relevant field is gaining traction in the healthcare space. Augmented intelligence – a concept that reflects the enhanced capabilities of human decision-making in clinical settings when augmented with computation systems and methods.