Innovation has become part of humans. Its sounds funny but humans are being addicted or totally dependent on the technology. Personally, I like the idea of innovation technology because it is the only way we can use our mind properly. Right or wrong? Its totally depend on the intentions of one being.
In the health sector, Machine Learning and AI become GOD. In this pandemic, some of the healthcare applications become the basic guide to the public.
The health care industry is always been the early adopters of innovation and technology. And nowadays Health care and AI are inseparable. From managing the endless data, personalised care, to technology advancement like mobile health care (mhealth). Machine learning achieves great heights in this sector.
Global health care research expected to increase by USD 10 Trillion by 2022.
All About Mhealth
As the usage of smartphone increased, the healthcare industry started to shift digitally.
There is an app for everything from maintaining a record to get the appointment.
The business has to think out of the box to achieve some great success in this industry.
Just like Netflix, Siri and Ok Google recommend us according to the choices of our searches, these are real-world examples of machine learning.
Google has developed ML Algorithm for cancerous tumours. Stanford using ML for Skin Cancer identification.
Process of ML is called “Training” of the machine and the output they produce is called “Model”.
Mhealth Applications Among Hospitals
In this pandemic, these applications will be very helpful for patients as well as for the doctors. Versatile applications permit suppliers to viably smooth out the correspondence between patients, suppliers, and their guardians and takes into account day in and day out the administration of a patient’s condition alongside the capacity to customize medicinal services per persistent.
- AirStrip, AirStripOB/Cardiology: an interoperable stage that permits care coordination between different gadgets and various consideration settings. This application helps providers to compact all data into one platform, accessed via telemedicine, and other vendor systems.
- Aetna, ITriage: patients directly find their health condition on this application and it also provides step by step guide to it. Does it also show that weather there conditions required to visit the ICU or not? Yes, it does show.
- Cerner, CareAware Connect: it manages workflows and clinical communication in one device.CareAware can be used with bar codes to manage medication administration and similar tools to associate patients in a mobile directory.
- Spok, Spok Mobile: provide a uniform place to co-ordinate the workflow and appointments. Its a kind of messaging system which has a feature of delivered/read. By chats, they keep in touch with the care team and patients.
Machine Learning in Healthcare Sector
Identification of disease and diagnosis:
ML is helping the doctors to identify the diseases and also suggest some steps to take in order to prevent it. It diagnoses from minor disease to severe cancer. This is the power of Machine Learning.
Example: QuantX powered by AI and ML. It tends to the basic needs of patients and practice heads and gives data that empowers quicker and increasingly exact determination, individualized treatment, and improved results. The objective of this advancement was to give better outcomes and improved determination by radiologists for the patients.
Drug Discovery and Manufacturing:
Machine Learning Algorithm can identify patterns in the data. Developing new drugs consumes time and money. There are various components that are scrutinized and just one outcome can end up being helpful. With the progressions in innovation, AI can prompt invigorating this procedure.
Example: Insitro (California based startup) they developed new drugs and medicines to cure patients. They use a combination of data science from Machine learning to AI, AI to advance laboratory technology.
Project Hanover by Microsoft also using AI and ML for cancer treatments and personalised drug combination for the patient.
Techniques, as x-beam and CT examine, were sufficient to review minor anomalies, yet with the expanding diseases, there was a need to assess them appropriately. With the assistance of AI strategies, for example, profound learning, it is currently conceivable to discover tiny disfigurements in the examined pictures inside the patients and thus, specialists can recommend an appropriate determination.
Example: Microsoft’s InnerEye project which works for making tools for image analysis. InnerEye is an examination venture that utilizations AI innovation to manufacture imaginative devices for the programmed, quantitative investigation of 3D radiological pictures. This project helps to distinguish between tumours and healthy anatomy and even assists experts in the field of radiotherapy and surgical planning.
With the blast of patient information as hereditary data and electronic wellbeing records, specialists can give customized treatment to singular patients as per their exact needs.
Example: Watson healthcare IBM Watson’s project. This is making incredible assets for improving the soundness of the patient. Watson human services diminished the hour of specialists that was spent on settling on treatment choices by furnishing them with individualized treatment choices dependent on the investigation of late examination, clinical practices, and preliminaries. Also offers treatment for Blood Cancer.
Smart Health Records:
MI entered to save time, money and effort by maintaining such huge records.
Example: Ciox, a European health technology company uses ML for maintaining the records of the patient, and improve the accuracy of health information.
Individuals need to quit considering AI as an idea from the future and rather, grasp the apparatuses and openings it is making accessible for us. These uses of AI are propelling the field of medication into a totally new space which makes it energizing to consider where it can go later on.
Any inquiries in the Benefits of Machine Learning in Healthcare? Offer your perspectives in the remarks.