How AI Is Crunching Big Data To Improve Healthcare Outcomes
Source – psfk.com
The state of your health shouldn’t be a mystery, nor should patients or doctors have to wait long to find answers to pressing medical concerns. In PSFK’s Future of Health Report, we dig deep into the latest in AI, big data algorithms and IoT tools that are enabling a new, more comprehensive overview of patient data collection and analysis. Machine support, patient information from medical records and conversations with doctors are combined with the latest medical literature to help form a diagnosis without detracting from doctor-patient relations.
The impact of improved AI helps patients form a baseline for well-being and is making changes all across the healthcare industry. AI not only streamlines intake processes and reduces processing volume at clinics, it also controls input and diagnostic errors within a patient record, allowing doctors to focus on patient care and communication, rather than data entry. AI also improves pattern recognition and early diagnosis by learning from multiple patient data sets.
By utilizing deep learning algorithms and software, healthcare providers can connect various libraries of medical information and scan databases of medical records, spotting patterns that lead to more accurate detection and greater breadth of efficiency in medical diagnosis and research. IBM Watson, which has previously been used to help identify genetic markers and develop drugs, is applying its neural learning networks to help doctors correctly diagnose heart abnormalities from medical imaging tests. By scanning thousands of images and learning from correct diagnoses, Watson is able to increase diagnostic accuracy, supporting doctors’ cardiac assessments.
Outside of the doctor’s office, AI is also being used to monitor patient vitals to help create a baseline for well-being. By monitoring health on a day-to-day basis, AI systems can alert patients and medical teams to abnormalities or changes from the baseline in real time, increasing positive outcomes. Take xbird, a mobile platform that uses artificial intelligence to help diabetics understand when hypoglycemic attacks will occur. The AI combines personal and environmental data points from over 20 sensors within mobile and wearable devices to create an automated personal diary and cross references it against blood sugar levels. Patients then share this data with their doctors in order to uncover their unique hypoglycemic triggers and better manage their condition.
In China, meanwhile, web provider Baidu has debuted Melody, a chat-based medical assistant that helps individuals communicate their symptoms, learn of possible diagnoses and connect to medical experts. Melody guides patients in providing enough information to assess the scope and urgency of their condition. The app then pulls from medical texts, search queries and past communications to identify possible diagnoses and offer preliminary health information or connect users with local doctors for further appointments and care. Powered by Baidu’s deep learning and natural language processing networks, Melody improves her communication and diagnostic skills by learning from conversations with Baidu’s hundreds of millions of users.