While AI in healthcare can offer endless opportunities — seizing them might require a longer wait.
There exist a number of potential ways to use Artificial Intelligence in healthcare, and it might become a growth engine for the sector, sooner or later. However, in areas such as personalized medicine, these applications are still very far from mass adoption. The current uses of AI in healthcare are mainly limited to detecting patterns and deviations and creating data-driven forecasts.
Artificial intelligence has been studied for the past forty years, but it is now experiencing a revival as a result of many factors. AI is a broad term that refers to machines that have the ability to respond to situations in a way that resembles human intelligence. One of its subfields is machine learning, which refers to devices learning from repetition without being explicitly programmed to do so. The rise of AI is enabled not only by new and more efficient computing capacity but also by creative solutions and open source tools produced by software developers.
How Can AI Be Applied in Healthcare?
Until now, AI has not transformed health care by extorting patterns from big data or solving difficult cases. AI is an exceptional tool for the kinds of tasks based on correlation, but it is less effective when it comes to causality. The current use of AI in healthcare mainly involves speeding up day-to-day routines, identifying patterns as well as deviations from them, and performing data-driven forecasts.
Sequentially, AI is merely a tool whose effectiveness is determined by how it is used. Algorithms and models require highly competent people to create them, followed by immense amounts of high-quality data.
One of the first areas of healthcare where AI is highly likely to have useful applications is diagnostics. For example, AI can be used to detect differences in the retina of a diabetes patient and a healthy individual, and deep learning will make it possible to use retinal images in the diagnosis of cardiovascular disease in a few years’ time.
The next most likely area of application is care management. AI can be used to access and combine patient data from many different sources, such as hospitals and healthcare systems. The use of multiple sources and signals makes it possible to produce more accurate forecasts at an increasingly early stage.
Personalized medicine is the third area of application, although it cannot be expected in the near future.
According to a recent report findings conducted by Accenture, the top three AI applications with the highest value potential in health care are robot-assisted surgery, virtual nursing assistants, and administrative workflow assistance. The estimated valued in these are between $40-$18 billion.
While these kinds of advancements can reduce human error and boost overall outcomes, many still question the practical applicability of AI in health care. Patients and caregivers fear that lack of human oversight and the potential for machine errors can lead to future problems. Data privacy remains one of the most significant challenges to AI-dependent health care.
Despite many concerns, the growing involvement of AI in health care is inevitable, as the potential benefits will likely outweigh the risks.
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