Artificial intelligence and machine learning continues to become applicable in health care such as developing new treatments and increasing the human lifespan through quality care. Medical imaging works through deep learning models that enable health specialists to understand the real issues affecting patient health and offering the right treatment. Consequently, the health care industry is adopting AI given the high accuracy achieved through these technological solutions. At the same time, we should not be overexcited by these AI developments in health care because of the bias concerns raised from meta-analysis reviews.

Disease diagnosis is probably an important development of AI in health care as patient health outcomes continue to improve significantly. The approval of over 30 AI-based algorithms by the FDA sheds light on the promising future of AI in the health care sector. Medical clinics apply AI technology to review, diagnose, and treat ailments facing patients with much success. The Lancet Digital study is one example of research conducted on the accuracy of deep learning in medical imaging where the researchers compared outcomes with human health professionals. After using sample studies within the 2012–2019 range, it became clear that AI offers better diagnostic information on diseases compared to humans.

Despite challenges faced in the application of deep learning algorithms in detecting diseases, there is still hope considering the potential held by AI. The success of AI-based automation in health care depends on the execution of these algorithms in real-world situations to match the current demand. Testing models of deep learning to minimize errors and bias is an example that best suits the health care industry because of achieving quality patient care. Measuring variables such as the implementation of deep learning algorithms will change the course of health care for the better because of understanding the implications and results.

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