Artificial Intelligence in Healthcare

 Artificial Intelligence for Healthcare

 The last two years have shown us how neglected and overburdened our healthcare system is, starting from gross inequality between rural and urban healthcare, lack of infrastructure, shortage of trained and efficient manpower, and, unmanageable load on the infrastructure as the patient to doctor ratio is 1:1511 and nurse to patient ratio is 1:670. 

This situation has made it necessary to bring about a drastic transformation in the healthcare sector of India to be able to mitigate any such unpredictable outbreak in the future. And a major part of that transformation is digitizing the healthcare sector.

AI and CT scans

 Artificial intelligence (AI) played a very important role in the management of Covid 19 as AI was used for the qualification and detection of COVID-19 cases from CT scans and X-ray images. Researchers have built a model of deep learning called (COVNet) COVID-19 Detection Neural Network, which helped in differentiating between pneumonia acquired from within a community and COVID-19 based on visual 2D and 3D characteristics which are drawn out from high-accuracy chest CT scans.

Warning systems

AI helped in predicting the spread of the virus and in building an early warning system by drawing out information from various social media platforms, news sites and informed about the regions that are vulnerable and also helped in forecasting morbidity and mortality. BlueDot an outbreak intelligence platform identified a group of pneumonia cases and forecasted the geographical position and outbreak of the COVID-19 based on available data by the use of machine learning. HealthMap is a platform that collects data that is available publicly and makes it accessible to help in the tracking of its spread.

A triage system based on AI helped in reducing the burden of the work of healthcare workers and medical staff through the automation of various processes like determining the mode of care and treatment and providing training to practitioners by analyzing medical data using the approach of pattern recognition, digitization of the patient’s reports and also by providing a solution that lessens contact with patients. 

Genetics

AI can also be used to classify patients based on genetic disposition, the intensity of symptoms, and medical reports in various categories like severe, moderate, and mild which can help in identifying the approach for patient managing patients in the most accurate manner.

Telemedicine

 In telemedicine, AI can put an end to the need for unnecessary and frequent hospital visits by monitoring cases from a distance and recording the data of patients who are asymptomatic or who have mild symptoms. Chatbots based on AI can be used for consultation, which can avoid people crowding the hospital and reduce the spread of infection thus preventing the weighing down of operations of critical care services. 

AI can complement regular technologies by bringing down the time needed in bringing medicines from bench to bed by accelerating the speed of virtual screening, validation process, and lead discovery by a decent margin. AI can also speed up the pace by extracting useful data for repositioning and repurposing drugs through screening properties of drugs that are already validated and approved based on molecular properties and descriptors. BenevolentAI made use of machine learning to speed up its program of drug discovery and recognized Baricitinib as a prospective drug for COVID-19. 

Mis-information

Due to abundantly available information, people are highly susceptible to misinformation which can be deadly in this situation. Knowledge, understanding, practice, and awareness for COVID-19 by filtering out information from sources like Facebook, Twitter, etc can be of help in developing the strategy required to disseminate and assemble accurate information for reducing the effect of COVID-19. Machine learning can be used to recognize the sentiments and trends analysis and also provide information about the source of false information and assist in cutting down misinformation and rumors. It can also be used to present an accurate picture of the rates of recovery, availability, and accessibility to healthcare and also recognize the gaps. It can provide updates about the upcoming evidence in treatment, diagnosis, therapeutic outcomes, and spectrum of symptoms in a dynamic situation like this which can help the public in taming fear and panic and help clinics in a real-life scenario. 

As the present situation summons the requirement for instant delivery of solutions, response towards this outbreak was highly augmented by AI and numerous digital technologies. AI was on par and more precise than human experts in the diagnosis and discovery of drugs. There is a need for bigger datasets to train AI models and ethical considerations and a legal framework for the dissemination of data before AI takes the center stage in diagnosis. There are several gridlocks in harnessing the full potential of AI in the present situation like sharing and availability of epidemiological and clinical data, scalability, ethical concerns, privacy, and computational resources overcoming which AI can be used to its fullest in expanding and developing healthcare and preparing it for any kind of unpredictable situations like COVID-19