Machine Learning and Artificial Intelligence in Healthcare Industry
21st century conquered the whole world with new discoveries and the creation of innovative software. The world does not stand still, and information technologies are also progressing making impact on many industries. Healthcare is not an example. Particularly in the case of automation, machine learning and artificial intelligence, doctors, hospitals, insurance companies, and industries with ties to healthcare have all been impacted – in many cases in more positive and substantial ways than other industries. Let’s take a brief look at these ways. The list below is for sure not complete, but provides an outline of some of ML’s and AI’s applications in the healthcare industry.
1. Processing data
Data management is the most widely used application of ML, AI and digital automation in general, as the first step in healthcare is collecting and processing patient’s info, for example medical records or other past history. Machines collect, store and process data to provide a faster and handy access.
2. Remote Consultation
There is quite a number of mobile apps and systems that use AI and ML to give medical consultation based on personal medical history and common medical knowledge. Users submit their symptoms and condition into the app, which connects them with a relevant specialist. A more precise data regarding patient’s health condition (hearts rate, activity condition, etc.) can be provided by various wearable health trackers, such as FitBit, Apple, Garmin and others.
3. Virtual Nurses and medication management
A digital nurse Molly was developed to help people monitor patient’s condition and follow up with treatments, between doctor visits. The program uses machine learning to support patients, specializing in chronic illnesses. The AiCure app, in its turn monitors the use of medication by a patient. A smartphone’s webcam is partnered with AI to autonomously confirm that patients are taking their prescriptions and helps them manage their condition. Most common users could be people with serious medical conditions, patients who tend to go against doctor advice, and participants in clinical trials. Combining these two apps can fully cover the in-house treatment of patients, making hospital stay not necessary.
4. Drug Creation
Developing new drugs or vaccinations by conducting clinical trials can take more than a decade and cost billions of dollars. Making this process faster and cheaper could change the world. Curing Ebola virus infection can make a great example, when a software based on AI was used to scan existing medicines that could be redesigned to fight the disease. The program found two medications that may reduce Ebola infectivity in one day, when analysis of this type generally takes months or years – a difference that could mean saving thousands of lives.
5. Robotic Surgery
The da Vinci robot has gotten the bulk of attention in the robotic surgery space, and some could argue for good reason. This device allows surgeons to manipulate dextrous robotic limbs in order to perform surgeries with fine detail and in tight spaces (and with less tremors) than would be possible by the human hand alone.
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.
Read Original Article On https://altabel.wordpress.com/2018/05/02/machine-learning-and-artificial-intelligence-in-healthcare/
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1. Processing data
Data management is the most widely used application of ML, AI and digital automation in general, as the first step in healthcare is collecting and processing patient’s info, for example medical records or other past history. Machines collect, store and process data to provide a faster and handy access.
2. Remote Consultation
There is quite a number of mobile apps and systems that use AI and ML to give medical consultation based on personal medical history and common medical knowledge. Users submit their symptoms and condition into the app, which connects them with a relevant specialist. A more precise data regarding patient’s health condition (hearts rate, activity condition, etc.) can be provided by various wearable health trackers, such as FitBit, Apple, Garmin and others.
3. Virtual Nurses and medication management
A digital nurse Molly was developed to help people monitor patient’s condition and follow up with treatments, between doctor visits. The program uses machine learning to support patients, specializing in chronic illnesses. The AiCure app, in its turn monitors the use of medication by a patient. A smartphone’s webcam is partnered with AI to autonomously confirm that patients are taking their prescriptions and helps them manage their condition. Most common users could be people with serious medical conditions, patients who tend to go against doctor advice, and participants in clinical trials. Combining these two apps can fully cover the in-house treatment of patients, making hospital stay not necessary.
4. Drug Creation
Developing new drugs or vaccinations by conducting clinical trials can take more than a decade and cost billions of dollars. Making this process faster and cheaper could change the world. Curing Ebola virus infection can make a great example, when a software based on AI was used to scan existing medicines that could be redesigned to fight the disease. The program found two medications that may reduce Ebola infectivity in one day, when analysis of this type generally takes months or years – a difference that could mean saving thousands of lives.
5. Robotic Surgery
The da Vinci robot has gotten the bulk of attention in the robotic surgery space, and some could argue for good reason. This device allows surgeons to manipulate dextrous robotic limbs in order to perform surgeries with fine detail and in tight spaces (and with less tremors) than would be possible by the human hand alone.
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.
Read Original Article On https://altabel.wordpress.com/2018/05/02/machine-learning-and-artificial-intelligence-in-healthcare/
Mobile Application Development Services
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