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AI AND HEALTHCARE  /
February 12, 2019
Viktoriya Polyarush Social Media Coordinator, an avid reader with the passion for technology.

Mentioning that the role of AI in Healthcare is dramatic will be an underestimation. AI and Machine Learning can introduce those changes that have an essential impact on healthcare processes and administration. While there is a lot we have to overcome to reach the stage of AI-reliant healthcare, there is sufficient potential in the technology today to push governments, healthcare institutions, and providers to invest in AI-powered solutions.

So far, Artificial Intelligence and Machine Learning are supposed to make the work of healthcare providers more logical and streamlined than repetitive. The technology is helping reshape personalized healthcare services decreasing the time to search for information that is critical to decision making and triggering better care for patients. Artificial Intelligence in Healthcare has immense potential to improve costs, the quality of services, and access to them.

Automated Image Diagnosis with AI/ML

Medical image diagnosis is another AI use case in Healthcare. Besides, one of the most essential trouble spots that medical practitioners have to encounter is handling with the volume of information available to them, thanks to EMRs and EHRs. This data also includes imaging data from procedure reports, pathology reports, downloaded data, etc. Moreover, in the future, patients will send even more data through their remote portals, including images of the wound site to check if there is a need for an in-person checkup after a treatment period.

These images can now be potentially scanned and assessed by an AI-powered system. X-rays are only one piece of the puzzle when it comes to medical imaging. We also have MRIs, CT scans, and ultrasounds.

Oncology and Pathology

Whenever we talk about Artificial Intelligence in Healthcare, we shouldn’t leave   deep learning aside. Researchers are using deep learning to train machines to identify cancerous tissues with the precision comparable to a trained physicist. Deep learning presents unique value in detecting cancer as it can help aim at higher diagnostic accuracy in comparison to domain experts.

Machine learning in Healthcare can help reshape the efforts in pathology often traditionally left to pathologists as they often have to work on multiple images in order to reach a diagnosis after finding any trace of deviations. With assistance from machine learning and deep learning, pathologists’ efforts can be streamlined, and the accuracy in decision making can be increased.

While these networks and AI-powered solutions can assist pathologists, we need to emphasize that artificial intelligence is not replacing physicians in this domain any sooner. Deep learning networks can only become so nifty when they get experience and learning over a period, just as physicians do.

AI in Healthcare, specifically in pathology, can help substitute the need for physical samples of tissues by improving upon the available radiology tools – making them more accurate and detailed.

Errors and frauds scar the landscape of healthcare. Therefore, one of the most critical of using AI in Healthcare is providing the security of data and possible solutions. Fraud and breach detection traditionally depended on reviewing systems manually. However, using Artificial Intelligence in Healthcare for monitoring and detecting security anomalies can create trust as the foundation for more digital disruption in the healthcare area.

Managing Data

AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognized opportunities for clinical practitioners and health service organizations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. With modern Medicine facing a significant challenge of acquiring, analysing and applying structured and unstructured data to treat or manage diseases, AI systems with their data-mining and pattern recognition capabilities come in handy. While leaving the communication of serious matters and final decision making to human clinicians, AI systems can take responsibility for routine and less risky diagnostic and treatment processes. The intention here is not to replace human clinicians but enable a streamlined high-quality healthcare delivery process. Healthcare delivery has over years become complex and challenging. A large part of the complexity in delivering healthcare is because of the voluminous data that is generated in the process of healthcare, which has to be interpreted in an intelligent fashion. AI systems with their problem solving approach can address this need. Their intelligent architecture, which incorporates learning and reasoning and ability to act autonomously without requiring constant human attention, is alluring. Thus the medical domain has provided a fertile ground for AI researchers to test their techniques and in many instances; AI applications have successfully solved problems with outcomes comparable to that of human clinicians. As healthcare delivery becomes more expensive, stakeholders will increasingly look to solutions that can replace the expensive elements in patient care and AI solutions will be sought after in these situations.

The role of Artificial Intelligence in Healthcare is not constrained by the mentioned above examples. As trends come up and physicians look for more innovative ways to improve healthcare services and experiences for patients, we will have novel concepts turning into reality. Despite the fact that the healthcare space is the fertile soil for changes, it will be a while before these systems can be made affordable and available to all healthcare institutions.