“A study by Accenture has predicted that growth in the AI healthcare space is expected to touch $6.6 Bn by 2021 with a CAGR of 40%.
A report by Juniper Research states that chatbots will be responsible for saving $8 Bn per annum of costs by 2022 for Retail, ecommerce, Banking, and Healthcare
The same research study also predicts that the success of chatbot interactions where no human interventions take place will go up to 75% in 2022 from 12% in 2017.
In 2017, Scanadu developed doc.ai. The application takes away one task from doctors and assigns it to the AI – the job of interpreting lab results.
Medical image diagnosis is another AI use case in healthcare. One of the most significant issues that medical practitioners face is sifting through the volume of information available to them, thanks to EMRs and EHRs.
Artificial Intelligence in Healthcare also talks about deep learning. Researchers are using deep learning to train machines to identify cancerous tissues with an accuracy comparable to a trained physicist.
Machine learning in healthcare can help enhance the efforts in pathology often traditionally left to pathologists as they often have to evaluate multiple images in order to reach a diagnosis after finding any trace of abnormalities.
Another similar solution is Moon developed by Diploid which enables early diagnosis of rare diseases through the software, allowing doctors to begin early treatment.
Cybersecurity has become a significant concern for healthcare organizations, threatening to cost them $380 per patient record.
The AiCure app developed by The National Institutes of Health helps monitor medication by a patient.”