There is a significant difference between automation and the use of artificial intelligence (AI) in the medtech space. Automation, within a healthcare setting, is defined as the use of hardware and software specifically programmed to save time. AI can be categorised as machine learning, meaning software and hardware working in conjunction to effectively mimic human decision-making—just much, much faster. AI can learn outside of its programming, and the goal is for the software to make a decision of equivalent quality to a human.
The use of automation and AI is integral within the medtech space, as data are becoming increasingly important to manage and understand. Data management is a multifaceted issue within the healthcare industry, but understanding and effectively utilising it can lead to better patient outcomes. In addition, automation can save time by replacing menial—and some intricate—tasks, allowing health practitioners to focus on more important tasks.
Currently, automation’s principal medical device-related use serves this very objective: to free up healthcare practitioners’ time to focus on more complex tasks that require a higher level of thinking. As data management becomes a prime directive for hospitals, clinics, and labs, there are numerous solutions to create a holistic solution for patients.
A paragon of automation within the healthcare space is electronic health records (EHR); not only do these programs have full patient profiles readily available, they can also incorporate patient tests—such as MRIs, X-rays, and in vitro diagnostics—directly onto the record from testing departments. This allows patients to move departments within a hospital while their full record follows them for easier clinical decision-making. However, EHR management programs in medtech suffer from low connectivity to other EHR management systems, as well as a lack of interoperability.
AI integration within the healthcare space is to provide machines that mimic human thinking, specifically that of a physician. Currently, multiple applications of AI are focusing on image analysis, saving thousands of hours while learning trends in order to identify disease states within a patient population. AI machine learning algorithms will be used to detect patients registered with benign indications, such as abdomen pain, and to detect severe issues, such as systemic infections or intestinal proliferation.
Figure 1 illustrates the relationship between automation and AI; low automation and low AI are commoditised medical devices. The market is dominated by automation companies, as hospitals and health institutions need to keep costs at a minimum while increasing data management and automating repetitive tasks.
Figure 1: qualitative measure of some companies increasing the level of automation and AI in healthcare
Automation is widespread within healthcare, with AI to follow as more companies move towards AI development and integration. Eventually, this will lead to AI systems being layered on top of automated devices, leading to intelligent automation.
Intelligent automation will combine the prowess of automation with the cognitive abilities of AI. As more stakeholders are involved with automation, AI, and general data management within a healthcare context, the move towards a combination of all three aspects will create healthcare solutions equivalent to sci-fi aspirations.