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<div class="padding"><strong>Artificial intelligence in healthcare</strong> is an overarching term used to describe the use of machine-learning algorithms and software, or <a title="Artificial intelligence" href="https://en.wikipedia.org/wiki/Artificial_intelligence">artificial intelligence</a>(AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
<p>The primary aim of health-related AI applications is to analyze relationships between clinical techniques and patient outcomes.<sup id="cite_ref-1" class="reference"><a href="https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare#cite_note-1">[1]</a></sup> AI programs are applied to practices such as <a title="Medical diagnosis" href="https://en.wikipedia.org/wiki/Medical_diagnosis">diagnostics</a>, <a title="Medical guideline" href="https://en.wikipedia.org/wiki/Medical_guideline">treatment protocol</a> development, <a title="Drug development" href="https://en.wikipedia.org/wiki/Drug_development">drug development</a>, <a title="Personalized medicine" href="https://en.wikipedia.org/wiki/Personalized_medicine">personalized medicine</a>, and <a class="mw-redirect" title="Patient monitoring" href="https://en.wikipedia.org/wiki/Patient_monitoring">patient monitoring</a> and care. What differentiates AI technology from traditional technologies in healthcare is the ability to gather data, process it, and produce a well-defined output to the end-user. AI does this through <a title="Machine learning" href="https://en.wikipedia.org/wiki/Machine_learning">machine learning</a> <a class="mw-redirect" title="Algorithms" href="https://en.wikipedia.org/wiki/Algorithms">algorithms</a> and <a title="Deep learning" href="https://en.wikipedia.org/wiki/Deep_learning">deep learning</a>. These processes can recognize patterns in behavior and create their own logic. To gain useful insights and predictions, machine learning models must be trained using extensive amounts of input data. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: once a goal is set, the algorithm learns exclusively from the input data and can only understand what it has been programmed to do, (2) and some deep learning algorithms are <a title="Black box" href="https://en.wikipedia.org/wiki/Black_box">black boxes</a>; algorithms can predict with extreme precision, but offer little to no comprehensible explanation to the logic behind its decisions aside from the data and type of algorithm used.<sup id="cite_ref-:8_2-0" class="reference"><a href="https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare#cite_note-:8-2">[2]</a></sup></p>
<p>As widespread use of AI in healthcare is relatively new, research is ongoing into its application in various fields of medicine and industry. Additionally, greater consideration is being given to the unprecedented ethical concerns related to its practice such as data privacy, automation of jobs, and representation biases.<sup id="cite_ref-3" class="reference"><a href="https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare#cite_note-3">[3]</a></sup></p>
<p>Recent developments in <a title="Statistical physics" href="https://en.wikipedia.org/wiki/Statistical_physics">statistical physics</a>, <a title="Machine learning" href="https://en.wikipedia.org/wiki/Machine_learning">machine learning</a>, and <a title="Inference" href="https://en.wikipedia.org/wiki/Inference">inference</a> algorithms are being explored for their potential in improving medical diagnostic approaches.<sup id="cite_ref-76" class="reference"><a href="https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare#cite_note-76">[76]</a></sup> Combining the skills of medical professionals and machines can help overcome decision-making weaknesses in medical practice.</p>
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ai	5
health	5
medicine	5
wpg	5

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