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Artificial Intelligence: the new healthcare algorithm

September 27, 2016 | Innovation, Treatment, diagnosis, digital health revolution, healthcare

The age of computers has hugely evolved, and with the emergence of effective machine learning, pattern recognition, and decision-making algorithms, Artificial Intelligence provides a revolutionary new approach to tailored healthcare. Associate Medical Copywriter Kulveer Singh dives deeper into the technology.

Artificial Intelligence (AI) is the science and engineering of making intelligent machines, and the art of using computers to understand human intelligence. When it comes to healthcare, AI can bring about radical changes to the way doctors and physicians access, communicate, and interact with patient data. Crucially, this can result in improved patient outcomes and even the possibility of using an individual patient’s history – such as previous illnesses, allergies or even genetic makeup – to predict their exact treatment requirements.

Associate Medical Copywriter Kulveer Singh explains how the introduction of AI can change the landscape of healthcare, why it’s an exciting time for the development of new technologies, and the challenges that implementing new AI may pose.

Better than humans? There’s proof!

In 2011, IBM’s supercomputer WATSON competed against and obliterated a long-term game show champion. All this, despite the general thought that computer programmes were unable to understand natural language, puns, red herrings, and to simply unpack the meaning behind game show questions.

With its ability to use natural language capabilities, hypothesis generation, and evidence-based learning, IBM WATSON has made the switch to healthcare and can support physicians in key decision-making processes. For example, an oncologist can use WATSON to assist in diagnosing and treating cancer patients. First, they might pose a query to the system, describing symptoms and other related factors (e.g. a patient has been suffering from severe and unexplained tiredness). WATSON begins by identifying the key pieces of information before mining through patient-related data to find relevant facts about family history, current medications, and other existing conditions. It combines this information with current findings from oncology clinical tests, doctors’ notes, treatment guidelines, instrument data, and scientific literature. It then examines these sources before forming hypotheses and testing them. WATSON will then provide a list of possible diagnoses with an accompanying score that indicates the level of confidence for each hypothesis. This contextual analysis allows WATSON to address complex problems, helping the doctor make more informed and accurate decisions.

Even Google with their DeepMind Health AI technology, have entered into the first NHS research project investigating how machine learning can help analyse ophthalmic scans, leading to earlier detection and intervention for patients with macular deterioration.

Download the full article from Blue Latitude Health

This content was provided by Blue Latitude Health