Artificial Intelligence Improving Health Care for Everyone

2nd August 2019 441 views Darlington Ehochi

Artificial intelligence improving Healthcare

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. The term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations.


Artificial Intelligence was initially conceptualized in the 1950s with the goal of enabling a machine or computer to think and learn like humans. AI is widely used by companies like Facebook (e.g.  recognising who is in a photo), and Google (e.g. providing search suggestions, or identifying the fastest route to drive).  However, in the healthcare industry, AI has only made small steps towards a vast and multidimensional opportunity.


Is the use of complex algorithms and software to estimate human cognition in the analysis of complicated medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input. What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms. These algorithms can recognize patterns in behavior and create its own logic. In order to reduce the margin of error, AI algorithms need to be tested repeatedly.


There are various capacities where AI is emerging as a game-changer for healthcare industry. Below are a few examples in use today:

  • Radiology - AI solutions are being developed to automate image analysis and diagnosis.  This can help highlight areas of interest on a scan to a radiologist, to drive efficiency and reduce human error. There is also opportunity for fully automated solutions – to automatically read and interpret a scan without human oversight – which could help enable instant interpretation in under-served geographies or after hours. Recent demonstrations of improved tumor detection on MRIs and CTs are illustrating the progress towards new opportunities for cancer prevention. Meanwhile, a company in the USA has already received FDA clearance for an AI-powered platform to analyze and interpret Cardiac MRI images.

  • Drug Discovery - AI solutions are being developed to identify new potential therapies from vast databases of information on existing medicines, which could be redesigned to target critical threats such as the Ebola virus. This could improve the efficiency and success rate of drug development, accelerating the process to bring new drugs to market in response to deadly disease threats.

  • Patient Risk Identification - By analyzing vast amounts of historic patient data, AI solutions can provide real-time support to clinicians to help identify at risk patients. A current focal point includes re-admission risks, and highlighting patients that have an increased chance of returning to hospital within 30 days of discharge. Multiple companies and health systems are developing solutions at present based on data in the patient’s electronic health record, driven in part by increasing push back from payers on covering hospitalization costs associated with re-admission. Other recent work has demonstrated the ability to predict risk of cardiovascular disease based purely on a still image of a patient’s retina.

  • Primary Care/Triage - Multiple organizations are working on direct to patient solutions to triage and give advice via a voice or chat-based interaction. This provides quick, scalable access for basic questions and medical issues. This could help avoid unnecessary trips to the GP, reducing rising demand on primary healthcare providers – plus, for a subset of conditions, provide basic guidance that otherwise wouldn’t be available for populations in remote or under-served areas. While the concept is clear, these solutions still need substantial independent validation to prove patient safety and efficacy.

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