Artificial intelligence (AI) has been playing a critical role in industries for decades. AI has only recently begun to take a leading role in healthcare. Our population is evolving and so the need for effective healthcare. Data generated as a result is huge and is almost impossible to be dealt with physically. In steps technology: AI along with Machine Learning (ML) aims to solve this problem. This greatly improves the efficiency of healthcare operations.
The combination of AI and ML has several uses in healthcare. Solutions are being developed to automate image analysis and diagnosis. This can help in highlighting areas of interest on scans to radiologists, to drive efficiency and reduce human error. Besides radiology, it also finds its use in drug discovery. Using the vast medical databases new medicine could be developed to tackle viruses like Ebola. By analysing the vast amount of prior patient data, quicker diagnosis and remedies could also be obtained as a result.
However, there are certain limitations that Artificial Intelligence and Machine Learning could potentially face in this sector. There may be doubts cast over the use of personal data and the ethics of an institution could come into question. Besides, the quality of the available data may not be up to the mark. In such a case, an additional step to refine the data comes in. As a result, the main purpose of efficiency is lost. The best opportunities for Artificial Intelligence and Machine Learning in healthcare over the next few years are hybrid models, where clinicians are supported in diagnosis, treatment planning, and identifying risk factors, but retain ultimate responsibility for the patient’s care. Byron Wallace, assistant professor of Computer and Information Science at Northeastern University thinks that the combination of Artificial Intelligence and Machine Learning could help us reach a point where data-driven patient care and healthcare policies could become a reality.
Harman Singh
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