Artificial Intelligence (AI) has taken the healthcare arena by a storm to improve the way care is being provided to the patients. While one can say that there is much more to accomplish in AI’s influence over healthcare, but the concerns for data privacy and fear of mismanaged care due to machine error and lack of human oversight has been on a constant check. Today AI-powered tools and solutions are assisting the healthcare arena there are numerous ways in which AI is supporting medical diagnostics.
Determination of Medical Imaging
Computer vision can be said as one of the most extraordinary breakthroughs, and this has been made possible through machine learning and deep learning, and this can be done through an unusually active healthcare application. Microsoft’s InnerEye program which was incorporated in 2010 is currently working on image diagnostic tools, and the team has posted numerous videos explaining their progress. It is estimated that deep learning is most likely play a more crucial role in diagnostic applications as deep learning, which is becoming more available, and as more data sources are made part of the AI diagnostic process.
Crowd Sourced Medical Data Compilation
There is an immense amount of focus on putting together data from different mobile devices to amass and make meaning of more vital health data. Apple’s research it is aspiring to do this in their path towards finding a cure of Parkinson’s disease and Asperger’s syndrome by permitting users to contact interactive apps that review their status over time; their use of the application feeds continuous progress data into an unsigned pool for a prospective study. IBM is also going to great lengths to obtain all the health data it can get its hands on, from partnering with care providers to buying out healthcare analytics firms, all to enhance their study.
Although much of the healthcare industry is a quagmire of laws and confusing incentives of various stakeholders including hospital CEOs, doctors, nurses, insurance companies and patients, drug discovery stands out as a comparatively straightforward economic value for machine learning healthcare application creators. This application also covenants with one relatively clear customer who happens to have an excellent financial strength which is drug companies usually.
A famous set of robots are garnering attention in the robotic surgery space, and some could dispute for a good reason. This device permits surgeons to influence dextrous robotic limbs to execute surgeries with fine detail and in tight spaces than it could be probable by the human hand alone. While not all robotic surgery actions involve machine learning, some systems use computer vision which is assisted by machine learning to recognize distances or a specific body part. Also, machine learning is in some cases can be used to stabilize the shifting and movement of robotic parts when taking directions from human controllers.
Personalized Medicine: Tailored made medicine based on a patient’s medical history, past condition, stress level, diet, and genetic lineage is the future of medicine and will be accepted with open arms.
Automatic Treatment or Recommendation: This is where AI can adjust a patient’s dosage of medicine by tracking their blood, stress, sleep, and diet. It may also contact the doctor automatically in case the patient’s health deteriorates or if they skip medications.
Improving Performance: While western medicine has kept its primary focus on treatment and amelioration of a disease, there is an immense need for proactive health interception and interference, and the first wave of IoT devices the likes of Fitbit is pushing these applications forward.
One can imagine that disease foilage or athletic performance will not be the only applications for health-promotion. Machine learning may be executed to track worker performance or stress levels on the job, as well as for seeking definite improvements in at-risk groups and not just reducing symptoms or healing after impediments.