AI

For healthcare decision-makers, investors, governments, and innovators, AI is at the top of their minds. AI can help patients to manage themselves through online symptom checkers and e-triage AI tools. The tasks that are typically done by humans are now done in less time and a fraction of the cost by Artificial Intelligence. It makes the lives of patients, doctors, and hospital administrators easy.

AI helps in improving healthcare operations by optimizing scheduling or bed management,  population health by predicting the risk of hospital admission, detecting early specific cancer symptoms which allows intervention leading to better survival rates; and optimizing healthcare R&D and pharmacovigilance.

There are certain AI applications based on imaging that are already in use in specialties such as radiology, pathology, and ophthalmology. AI solutions that support the shift from hospital-based to home-based care, such as remote monitoring, AI-powered alerting systems, or virtual assistants, are being used in increased numbers by the healthcare sectors. This also includes a broader use of NLP solutions in hospitals and home settings, and more use of AI in a broader number of specialties, such as oncology, cardiology, or neurology. With all these AI is seen as an integral part of the healthcare value chain.

Recent AI developments in healthcare 

Electronic Health Records (EHRs)

It is basically a digital version of a doctor's note, a record that contains a patient's medical history, diagnosis, and health journey throughout the years. At first, it is usually collected as a note during a patient's visit, and then it is entered into a database that keeps information in a structured format.

First Healthcare Interoperability Resources (FHIR)

FHIR uses a modern suite of APIs, HTTP-based RESTful protocols, HTML, and CSS for UI integration and allows the use of JSON, XML, or RDF for data representation. FHIR is becoming the top-rank protocol used by companies like Google and Apple. The main goal of this resource is to accelerate legacy healthcare systems to easily and smoothly provide information to medical providers and individuals.  With the adoption of FHIR, healthcare providers like hospitals and other private practices are now being able to produce a huge amount of data such as biographical data, diagnoses information, procedures, lab tests, and much more. Medical providers are using machine learning techniques to extract insights from data.

Diagnosis Prediction

Another AI development in the healthcare sector is early diagnosis prediction. This area uses machine learning models to envisage a disease at its inception or even before it. This is accomplished by training ML models with large quantities of labeled information.

Telemedicine 

Through the use of AI models, many companies are beginning to develop solutions that enable patients to receive a level of treatment without leaving their homes,

 which is similar to going to a hospital. The development of AI Chatbots caters to provide medical guidance to patients. These chatbots receive messages from the users discussing their current health issues, get that information as input and search for the most likely disease that would cause such symptoms. This process is called “Triage” and is seen to be more effective than performed by doctors or nurses. All areas of the healthcare sector are either directly or indirectly affected by the developments of recent AI models.

Managing Patient Diagnosis

AI provides the ability to accurately diagnose diseases at an earlier stage, envisage disease and disease progression at a personal level and incorporate caregivers earlier in the patient journey thereby improving patient care. Artificial Intelligence has been applied to various diagnostic aspects of several cancer types, which include breast cancer, blood cancers, and non-small cell lung cancer.

Drug Development 

Regular drug development is a lengthy process that is costly and time-consuming. AI and big data-driven approaches are used to eliminate these challenges and increase efficiencies by bringing life-saving medicines at a faster rate. With the application of Artificial Intelligence, bringing new drugs to market will be greatly shortened resulting in lower development costs. With reduced development costs and accelerated timelines for development, the ultimate cost of the healthcare system will also decrease while improving the benefit to the patients.