Many experts see the development of truly human-like artificial intelligence (AI) as an inevitable outcome of the drive to integrate technology into everyday life and improve quality of living. Due to the significant amounts of health and lifestyle data that are now easily accessible via the internet, the concept of machine learning – the model of giving machines data and structuring them to learn what to do rather than teaching them processes manually through coding – has gained popularity and expanded the limits of AI technology, making it an increasingly worthwhile investment for the healthcare industry.
In particular, because the growing availability of data has become the bedrock for care initiatives and organizational investments, AI can provide significant assistance in organizing and analyzing the data to provide valuable insights into how to best provide care. As a result of the increased usability of AI and machine learning, many organizations are looking to implement AI-driven initiatives, and vendors are developing and offering the technology to assist.
Rising Interest from Vendors and Health Organizations
The Machine Learning as a Service (MLaaS) market – a range of services that offer machine learning within cloud computing – is expected to grow from $480.94 million in 2015 to $5,394.87 million by 2022, according to a 2016 report by Stratistics MRC. The report identified the healthcare industry as the likely driver of future machine learning growth and innovations, aided by factors such as the Internet of Things and cloud computing. This growth is exemplified by some organizations announcing plans to include AI tools to promote machine learning in the healthcare industry. Furthermore, prominent organizations such as Microsoft, Google, and IBM Watson have already taken aim at blindness, acute kidney injuries, and cancer, respectively, with their own investments into AI and machine learning.
Success stories, like that of Indiana University-Purdue University Indianapolis, have brought further attention to the impressive outcomes of AI and machine learning initiatives. The university implemented an AI and algorithm that correctly predicts relapse rates for acute myelogenous leukemia with 90% accuracy and remission rates with 100% accuracy. Such case studies serve to underline the potential benefits of machine learning initiatives, making them even more desirable within the industry.
One of the biggest advantages of AI and machine learning is that they can be utilized to assist in a host of different initiatives. Among the most prominent uses is predictive analytics, such as the Indiana University-Purdue University Indianapolis initiative mentioned above as well as many others, in which machines analyze patient behavior and health data, find trends in cause and effect, and utilize that information to predict when a person may require preemptive care. Similarly, predictive analytics has been shown to be effective in managing and predicting trends in population health, especially when utilized in conjunction with patient-owned devices including cell phones and fitness trackers. As the technology improves, AI and machine learning may be able to push predictive analytics to allow care providers to eventually focus primarily on proactive care to provide preventative intervention before an ailment presents itself.
Machine learning can also be utilized to help structure unstructured data, such as analyzing wound images or intelligently reading old handwritten records and transforming them into characters for a digitized record. Similar technology can also be used to automatically classify paper documents based on the contained information as they are scanned, saving hospitals from the trouble of doing so manually. Furthermore, efforts in developing intelligent EHRs may result in diagnostic, clinical decision, and treatment suggestions all being computed from these records and displayed directly on the EHR.
Although true human-like AI is not expected to be possible for many more years, it is the area where some of the most exciting advancements, like machine learning, are being developed. As health organizations continue to gather large quantities of usable data from the internet, patient records, health trackers, and other devices, many organizations are realizing the advantages of utilizing AI to quickly interpret that data and transforming findings into actionable outcomes.
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