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Machine Learning in medicine has recently made headlines as it’s improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care.
As healthcare generates large data, the challenge is to collect this data and effectively use it for analysis, prediction, and treatment. Machine Learning allows building models to quickly analyse data and deliver results, leveraging the historical and real-time data. With ML, healthcare service providers can make better decisions on patient’s diagnoses and treatment options, which lead to overall improvement of healthcare services. Previously, it was challenging for healthcare professionals to collect and analyse the huge volume of data for effective predictions and treatments since there were no technologies or tools available. Now with Machine Learning, it’s been relatively easy, as Big Data technologies are mature enough for wide-scale adoption.
While not all robotic surgery procedures involve machine learning, some systems use computer vision aided by Machine Learning to identify distances, or a specific body part such as identifying hair follicles for transplantation on the head, in the case of hair transplantation surgery. In addition, Machine Learning is in some cases used to steady the motion and movement of robotic limbs when taking directions from human controllers.
The modern approach to healthcare is to prevent the disease with early intervention rather than go for a treatment after diagnosis. Traditionally, physicians or doctors use a risk calculator to assess the possibility of disease development. But with recent development in technologies such as Big Data and Machine Learning, it is possible to get more accurate results for disease prediction. Physicians are teaming up with statisticians and computer scientists to develop better tools to predict the diseases. Experts in the field are working on the methodologies to identify, develop, and fine-tune ML algorithms and models which can deliver accurate predictions. To develop a strong and more accurate Machine Learning model, we can use data collected from studies carried out, patient demographics, medical health records, and other sources.
Drug discovery and development is very costly and time-consuming work. Typically, a new drug development takes more than 10 years to get into a market and costs roughly around 2.6 billion dollars, according to the Tufts Center for the Study of Drug Development.
As pharmaceutical companies cannot predict a potential drug compound effect on targeted and non-targeted molecules using traditional computational technologies, the chances of drug failure are higher in clinical trials. This scenario makes drug discovery very costly and time-consuming process. Better predictive methods using machine learning can save a lot of resources in this case.
Machine learning based approach, considering the large volume of clinical data for approved and failed drugs, to identify a toxic compound that may cause side effects can save many resources before going into clinical trials.
Diagnosis is a very complicated process, and involves at least for now a myriad of factors of which machines cannot presently collate and make sense; however, there is little doubt that a machine might aid in helping physicians make the right considerations in diagnosis and treatment, simply by serving as an extension of scientific knowledge.
The promise of personalized medicine is a world in which everyone’s health recommendations and disease treatments are tailored based on their medical history, genetic lineage, past conditions, diet, stress levels, and more.
While eventually this might apply to minor conditions, it is likely to make much of its initial impact in high-stakes situations.
It is clear that Machine Learning puts another arrow in the quiver of clinical decision making. These are the few potential areas where Machine Learning can help the healthcare industry out of many scenarios. We see, with ML applications, healthcare and medicine segment can advance into a new realm and completely transform the healthcare operations. As larger datasets begin to run machine learning, we can improve care in more specific ways.