Technological advancements are changing every industry and healthcare is no exception: you can argue that the value of artificial intelligence (AI) is never greater than when it's used to improve patients' conditions and even save lives An example of how AI improves patient care is Amsterdam UMC's partnership with SAS. The project was able to clinically diagnose patients with colorectal liver cancer, the third most common cancer worldwide, using Computer vision and predictive analysis. Previously, this process required manual examination which was time-consuming and subjective to the radiologist. Automating this process has increased accuracy and saved time to ensure patient survival. Whether it's image analysis to detect cancer or other diseases immediately, predicting the number of patients to ensure the right number of doctors and hospital beds are available or using natural language processing (NLP) to understand lengthy patients reports – the potential for technological enhancement in healthcare is colossal.

Haidar Altaie, data scientist at SAS UK & Ireland, writes about how AI can be used to improve patient care, and ultimately save lives. Technological advancements are changing every industry and healthcare is no exception: you can argue that the value of artificial intelligence (AI) is never greater than when it's used to improve patients' conditions and even save lives. An example of how AI improves patient care is Amsterdam UMC's partnership with SAS. The project was able to clinically diagnose patients with colorectal liver cancer, the third most common cancer worldwide, using computer vision and predictive analysis. Previously, this process required manual examination which was time-consuming and subjective to the radiologist.

An example of how AI improves patient care is Amsterdam UMC's partnership with SAS. The project was able to clinically diagnose patients with colorectal liver cancer, the third most common cancer worldwide, using Computer vision and predictive analysis. Previously, this process required manual examination which was time-consuming and subjective to the radiologist. Automating this process has increased accuracy and saved time to ensure patient survival. Whether it's image analysis to detect cancer or other diseases immediately, predicting the number of patients to ensure the right number of doctors and hospital beds are available or using natural language processing (NLP) to understand lengthy patients reports – the potential for technological enhancement in healthcare is colossal.