Artificial Intelligence and Machine Learning – a leap of faith for healthcare?

Every industry in today’s time is affected by disruptive technologies, but medicine is one avenue that’s ripe for fundamental change. The 2009 HITECH Act mandated the widespread adoption of electronic medical records in the U.S. by legislating medical treatment into bits and bytes. We are poised for radical innovation facilitated by disruptive software, and particularly software capable of making use of large, complex datasets.

I believe that this shift will occur because of five key technologies – Artificial Intelligence (AI), Big Data, Blockchain, Robotics, and 3-D Printing. AI holds particular potential for improving medical care at the clinical level.

Digital technology has driven marvels and liberated physicians, nurses, and researchers to focus predominantly on higher-level cerebral tasks and patient care. Artificial Intelligence is composed to take this to the next level. The medical field must learn to delegate dreary, lower-level cognitive functions in order to allow medical professionals to focus more of their mental energy or higher-level thinking.

Herbert A. Simon captured a similar idea when he coined the phrase “Bounded Rationality”. The idea is that human decision making is at its best when people are given limited, relevant information and enough time to process the information.

Computers help enhance decision-making faculties by granting easier access to information that is critically relevant to a decision while sorting out non-relevant data. Humans spend less time trying to determine what information to look at and can spend more time applying the mind’s higher-level computational abilities to the information before us.

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As AI advances, it can add range to the power of human-thinking in three acute areas – advanced computation, statistical analysis. and hypothesis generation. These three areas resemble three distinct waves (paywall – wherein AI decides to illustrate content to people that we know need it. Briefly, it helps in filtering the audience in an improved way to have an enhanced seep-in rate.).

AI in the healthcare sector aims to improve patient outcomes by supporting healthcare practitioners in using medical knowledge, which has been methodically examined and memorized by these systems, thereby providing excellent clinical and medical solutions. AI systems will be capable of providing physicians and researchers with clinically relevant, real-time, quality information sourced from data stored in electronic health records (EHRs) for immediate needs.

The AI market for healthcare applications is expected to achieve rapid adoption globally, with a CAGR of 42 percent until 2021. Excellent patient outcomes, reduced treatment costs, and elimination of unnecessary hospital procedures with easier hospital workflows and patient-centric treatment plans are the prime reasons for the wide adoption and successive growth of the AI market in the healthcare industry.

By 2020, chronic conditions, such as cancer and diabetes, are expected to be diagnosed in minutes using cognitive systems that provide real-time 3D images by identifying typical physiological characteristics in the scans. By 2025, AI systems are expected to be implemented in 90 percent of the U.S. and 60 percent of the global hospitals and insurance companies. In turn, AI systems will deliver easily accessible, cheaper, and quality care to 70 percent of patients.

AI is continuously refining the method and entree to reliable and accurate medical image analysis with help from digital image processing, pattern recognition, and machine learning. For example, a startup from New York, Butterfly Network, has developed a handheld 3D ultrasound tool that creates 3D images of the medical image in real time and sends the data to a cloud service which identifies the characteristics and automates diagnosis. Such is the clinical provision expected from AI on the overall medical imaging diagnosis market and its growth.

Creative, mechanised patient direction and engagement solutions, such as AI-enabled medication observance to perceive patient devotion by using progressive facial recognition and motion-sensing software, have started to automate one of the major healthcare progressions of directly observed therapy (DOT). New applicants with similar solutions are expected to rapidly capture this sub-segment of the market.

The future healthcare applications for AI technology are frequent and exhilarating. Healthcare providers are discovering the application of AI programmes to insurance verification, skin cancer diagnosis, the analysis of lab results, and medical record data analysis. We have only started to catch promising glimpses of the horizons of the coming healthcare revolution.

 

 

 

 

 

 

 

 

 

https://yourstory.com/2018/07/ai-ml-healthcare-implications/



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