Bioinformatics

Integration of Machine Learning and AI in Healthcare

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Artificial intelligence in healthcare is the use of complex algorithms and software, or in other words, artificial intelligence (AI), to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of 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 and deep learning. These algorithms can recognize patterns in behavior and create their own logic. In order to reduce the margin of error, AI algorithms need to be tested repeatedly. AI algorithms behave differently from humans in two ways: 

  • Algorithms are literal: if you set a goal, the algorithm can’t adjust itself and only understand what it has been told explicitly
  • Some deep learning algorithms are black boxes; algorithms can predict with extreme precision, but not the cause or the why

Artificial intelligence simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.  

Incomplete medical histories and large caseloads can lead to deadly human errors. Immune to those variables, AI can predict and diagnose disease at a faster rate than most medical professionals. 

Application of AI in Healthcare

The drug development industry is bogged down by skyrocketing development costs and research that takes thousands of human hours. Due to breakthroughs in technology, biopharmaceutical companies are quickly taking notice of the efficiency, accuracy and knowledge that AI can provide.

One of the biggest AI breakthroughs in drug development came in 2007 when researchers tasked a robot named Adam with researching functions of yeast. Adam scoured billions of data points in public databases to hypothesize about the functions of 19 genes within yeast, predicting 9 new and accurate hypotheses. 

In the healthcare industry, time is money. Efficiently providing a seamless patient experience allows hospitals; clinics and physicians to treat more patients on a daily basis.

New innovations in AI healthcare technology are streamlining the patient experience, helping hospital staff process millions, if not billions of data points, faster and more efficiently. Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. 

The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also raised.

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