machine-learning-in-healthcare

Machine learning has acquired greater relevance in the last ten years. It is currently used in different industries, such as the automotive or entertainment sector. Social media services use machine learning algorithms to work. In the field of medicine, the potential is promising since it is possible to use it for the development and design of drugs, for diagnostics, and for medical care, however, the current evidence has not been enough to spread its use. There is no doubt that machine learning will have a beneficial role in healthcare and can push the boundaries of medical field.

Use of machine learning in the medical field

Deep learning techniques have been used since the beginning of the 21st century in modern hospitals, which are equipped with devices that gather and share large amounts of data in information systems; these data are applied for clinical analysis using artificial intelligence. Currently, studies show that artificial intelligence techniques can be more accurate than human assessment for diagnostics in the medical field.

Machine learning for diagnostics 

In the care process, the diagnostic phase is essential for patient orientation and follow-up. Machine learning brings new solutions to healthcare professionals to save time and optimize the correct diagnosis. It opens up new perspectives in the detection of diseases. For example, it can help doctors more easily detect abnormalities on patient x-rays. The objective is not to replace the doctor with the machine, but to support him in the analysis and interpretation of the enormous volumes of data collected. Machine learning also makes it possible to promote the right diagnoses and fight against medical errors by generating differential diagnoses and suggesting additional examinations.

Machine learning applied in surgery

Machine learning is now at the service of surgical interventions, in particular, to simplify the practice of surgeons or reduce surgical errors. Machine learning is used more and more in robotics. Computer-assisted surgery now makes it possible to improve the precision of gestures or to operate remotely. 

As an example, we can cite Da Vinci, undoubtedly the most advanced surgical robot in the world, especially for complex eye operations. Another example is Heartlander, a robotic caterpillar capable of entering a patient’s chest to assist human surgeons during heart surgery. Beyond the diagnostic part, the care of the patient or the doctor-patient relationship, the deployment of anamnesis assisted by artificial intelligence makes it possible to fight against diagnostic error, to provide a solution for medical deserts, or to promote patient involvement upstream.

Machine learning at the heart of R&D 

The pharmaceutical industry is increasingly impacted by machine learning, in terms of new drug discovery, clinical trials, distribution and marketing. Machine learning is now at the heart of R&D strategies, both in terms of molecular innovation and clinical trials. The crossing and processing of immense volumes of data make it possible to discover new molecules or to accelerate the phases of development. In terms of clinical trials, laboratories have difficulty finding volunteers who are clinically satisfactory and ready to commit to the duration of the protocol. Machine learning then appears as a solution to efficiently identify the right profiles, notably through the automatic analysis of information from medical reports, articles, patents, studies, etc.

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