AI systems are revolutionizing the healthcare industry, and the changes brought about by these systems are set to accelerate in the coming years. The potential of AI is so vast that it is predicted to become an integral part of the healthcare experience, with AI replacing or redefining most aspects of healthcare such as doctors, nurses, waiting rooms, and pharmacies. The COVID-19 pandemic has demonstrated that healthcare providers can pivot to adopt new strategies faster than expected, and the adoption of telehealth has further emphasized the need for AI in healthcare.
As per Big Think, there are two different approaches to AI, namely data-driven and knowledge-based. The data-driven approach involves the use of large amounts of data and computing power to derive complex models capable of accomplishing difficult tasks. On the other hand, the knowledge-based approach relies on domain expertise to build algorithms that apply approximations of accumulated human knowledge to execute logic on a fact pattern. The knowledge camp believes that domain expertise is critical for solving complex problems in areas such as human biology and disease.
At present, data-driven AI is more developed than knowledge-based AI, as the complexity of rules-based expert systems has been a significant impediment to scaling. Data-driven AI has been successful in solving complex problems in biology, such as helping to predict early wellness-to-disease transitions and predicting disease trajectories. However, domain expertise may be more important in helping us make sense of the complex signal-to-noise issues that arise in big data.
Processing power is also essential for AI systems to function. GPUs were initially developed for computer gaming to optimize the manipulation of images, but they have since been used to help neural networks bridge the gap between what the human brain evolved to do over millions of years and what computers have achieved over a matter of decades. By using GPUs to handle the hidden layers of input, processing, and output needed to create computer algorithms that can automatically improve themselves as they move through data, the speed of machine learning has increased a hundredfold.
Deep learning is another fundamental advance in neural networks’ algorithms, where artificial neural networks can contain tens or even hundreds of layers between input data and generated predictions. With each layer containing non-linear functions, deep learning networks can represent arbitrarily complex relationships among data, enabling them to discern patterns and make predictions from high-dimensional data.
However, there are limitations to the data-driven approach, with high-quality predictions often resulting in a “black box” decision whose logic we cannot fully comprehend. Data-driven AI can help us find functions that fit trends in data, but it cannot tell us anything new. The integration of data-driven and knowledge-driven approaches may be necessary to handle the extreme complexity of the human body.
In conclusion, AI systems are transforming the healthcare industry, and the changes brought about by these systems are set to accelerate in the coming years. While the data-driven approach has been successful in solving complex problems in biology, the integration of the data-driven and knowledge-driven approaches may be necessary to handle the extreme complexity of the human body. The potential of AI in healthcare is vast, and it is predicted to become an integral part of the healthcare experience.