2016 September 29 | Ziad Obermeyer, M.D., M.Phil. and Ezekiel J. Emanuel, M.D., Ph.D.
The article "Predicting the Future — Big Data, Machine Learning, and Clinical Medicine" by Ziad Obermeyer and Ezekiel J. Emanuel discusses the transformative potential of machine learning in medicine. The authors emphasize that while big data is essential, it is the algorithms that will drive significant change. Machine learning, unlike traditional expert systems, learns from data by identifying patterns and making predictions, even with a large number of variables. This approach can handle complex data, such as chest radiographs and insurance claims, and improve prognosis, radiology, and diagnostic accuracy. However, challenges include overfitting, data quality, and the need for causal inference. The authors predict that machine learning will disrupt three key areas of medicine: improving prognosis, replacing radiologists and pathologists, and enhancing diagnostic accuracy. They conclude that machine learning will become an indispensable tool for clinicians, ultimately benefiting patients through better care and outcomes.The article "Predicting the Future — Big Data, Machine Learning, and Clinical Medicine" by Ziad Obermeyer and Ezekiel J. Emanuel discusses the transformative potential of machine learning in medicine. The authors emphasize that while big data is essential, it is the algorithms that will drive significant change. Machine learning, unlike traditional expert systems, learns from data by identifying patterns and making predictions, even with a large number of variables. This approach can handle complex data, such as chest radiographs and insurance claims, and improve prognosis, radiology, and diagnostic accuracy. However, challenges include overfitting, data quality, and the need for causal inference. The authors predict that machine learning will disrupt three key areas of medicine: improving prognosis, replacing radiologists and pathologists, and enhancing diagnostic accuracy. They conclude that machine learning will become an indispensable tool for clinicians, ultimately benefiting patients through better care and outcomes.