Machine Learning in Clinical Research The market of applications and devices focused on health has grown on a large scale lately and specialists deduce that this change has taken place to such an extent thanks to the use of smartphones. One of the biggest problems for the application of artificial intelligence and machine learning in clinical research is the collection of information. If for example, you want to make an algorithm that predicts the possible outcome of a disease, you need a large number of medical records and most of them are not easily accessed at scale. New AI technology using the latest advancements in natural language processing are already able to make sense of ambiguity and detail in physician notes and make sense of this vast data at scale in short time frames. Artificial Intelligence is also helping medical billing - Recently, healthcare establishments have been combating the billing deficit with artificial intelligence (AI) software. When applied to bill and coding, sophisticated AI technology has the capability to contextualize unstructured data, compartmentalizing EHR data and connecting relevant information together.
Patient Information and Risk Analysis With artificial intelligence, documents such as medical guidelines can be improved. A medical guide is a document in which possible diagnoses are recorded given a list of symptoms. If these guidelines were powered by a learning algorithm, they could change depending on the successful diagnoses given by the doctors.
Diagnosis Through Image Analysis Many medical diagnoses can be made by a visual review of a patient or by laboratory results such as MRIs or radiographs. With AI, researchers using a large number of images of a condition may be able to detect the presence of that condition with great precision.
Healthcare Data Collection and Processing In healthcare, data often increases from one second to the next in an area that cannot be mastered by people alone and which is formally overwhelming. It is therefore hardly surprising that algorithms are now being used in the healthcare sector in order to collect the data on one hand and to subsequently process them as well. In this case, the influence of artificial intelligence will be demonstrated. Making use of analyzing data from billions of electronic healthcare records (EHRs) is one application set to save clinical care staff many hours of administrative time and put them back in front of patients.
Clinical Research In the field of diagnostics, the University of Stanford in California - for example - has impressively shown that an algorithm based on the principle of a neural network and deep learning has twelve different cardiac arrhythmia - such as atrial fibrillation and flutter, ventricular and supraventricular tachycardia, Sinus rhythm and measurement errors - not only more reliable but also diagnosed in a much shorter time than six experienced cardiologists using a single-channel ECG. This impressively showed that both the value of the sensitivity and the positive-predictive value of the algorithm were higher than the average values of the six cardiologists.
Clinical Trials If you take the advances of AI and the speed and accuracy that they can make sense of results often buried within big data, it's not hard to see how this can resolve a multitude of challenges for clinical trials. Working together with Artificial Intelligence technology and large datasets you analyze and synthesize this clinical trial critical information, These insights and applications for clinical research are far-reaching. The positive impact long and short term in clinical trials is only just starting to be realized.