AI's role in modern-day health care
With the impacts of COVID-19 being felt across the globe, the global healthcare sector has turned to technology to aid and accelerate innovation in the hope of combatting the pandemic.
The Australian Government has launched the COVIDSafe app to help digitally trace people who have come into contact with confirmed COVID-19 cases. Once the information is collected, artificial intelligence (AI) will examine and analyse the data, allowing the industry to better manage exposure to the virus.
There are countless other examples of AI and data analytics being applied in the fight against COVID-19. What’s important to note is this shift towards AI and data-led solutions isn’t unique to the current pandemic.
Historically, technologies like AI have been used to help reduce patient waiting times, support the development of custom-made medicines and reduce clinical disparity. Big data has also been used to manage previous worldwide health issues like the Ebola outbreak. Here are just a few examples of other ways AI is changing health care for the better:
Putting technology at the heart of healthcare operations: Healthcare staff are often under the microscope — from both a funding and performance perspective. To better manage demand, the industry is using historical data combined with AI to predict when more staff are needed in hospitals, for example, during flu season.
Minimising clinical disparity: Clinical variation is a huge industry concern as it leads to wasted supplies and impacts patient outcomes. Improving clinic variation requires assessing massive amounts of data, which is where AI can help. When supported by a solid IT infrastructure and significant computing power, AI can process this data at speed to reduce clinical disparity.
Using AI to fight cancer: Paige.AI is an organisation focused on improving clinical diagnosis and treatment in oncology through the use of AI. Traditionally, manual analysis is used for most pathologic diagnoses to treat cancer; however, AI can be used to examine complex data faster and more accurately, bringing us closer to a cure.
From a patient perspective, expectations of the healthcare sector have never been higher. Patients expect health services and health practitioners to assist them to be more informed, manage their health and provide prompt, appropriate and individualised help when required. On top of this, patients have increased expectations around data security, communication channels and access to digital medical records. Not to mention, as a result of the current pandemic, the healthcare sector has gone digital with a surge in telehealth appointments — no doubt there will be increased demand for similar offerings post-COVID-19.
In order to keep up with increasing and shifting healthcare demands, both from patients and the wider industry, technology, like AI and data analytics, is being used to support high-quality, sustainable healthcare services.
However, for AI and data analytics to continue improving healthcare operations, data management and analysis skills must become a crucial part of medical training. Staff must have an understanding of best practices when applying AI to health care; for example, sufficient and accurate data is crucial to success. Furthermore, datasets must be specific, accurate and sufficient enough to be free from bias — after all, an incorrect AI-driven decision could have deadly consequences.
Clearly, AI creates a variety of healthcare benefits, everything from advancing diagnosis accuracy to speeding up drug development. Implementing AI-driven solutions for health care still entails AI-related and clinical data challenges. However, with data being a catalyst for innovation, AI has the power to create real change within our healthcare system and solve current problems.
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