AI, Artificial Intelligence, Deep Learning Algorithms, cognitive computing- these techniques and terms have been the focus of the scientific community and laymen on the internet, respectively.
With its ability to mimic the human’s cognitive process and vast storage capacity, AI has been revolutionizing entire industrial and service sectors.
Capitalizing on the potential and capabilities, Google has trained its Artificial Intelligence to detect cardiovascular events through an eye scan. But how did Google achieve this? How does the process of integrating AI with Healthcare work?
Breaking Down the Process of Deep Learning Algorithms in Healthcare
Healthcare, in general, faces challenges like cost reductions in prevention and treatment, low cost-effectiveness, overutilization, inadequate patient care, and high readmission and mortality rates.
To overcome the barriers, doctors, healthcare institutes, and data scientists have collaborated to enable clinically meaningful automated, predictive data analysis and AI Systems.
With medical professionals’ help, scientists collect big data from mobile phone applications, wearable devices, sociodemographics, “omic” data, and EHRs to train AI. Predictive analytics & decision support systems are then built for doctors to correct diagnoses at the right time!
The process can be visualized, as seen in the image below.
Transformation in Cardiovascular diagnostics by Google AI
With its health-tech subsidiary Verily, Google has discovered a novel approach to assessing the risk of heart disease using deep learning. Their scientists have trained Google’s Artificial Intelligence using data from 28,355 patients by feeding the AI with their eye scans and general medical records.
The deep learning algorithm scans the patient’s rear interior wall of the eye (the fundus). By analyzing the same, it can accurately deduct the person’s age, gender, blood pressure, and smoking status. Using this data; the AI can further predict the patient’s cardiovascular disease risk using optic disks and blood vessels.
Future of Google AI and Other Machine Learning Algorithms in Combating Cardiovascular Diseases
Currently, Google’s algorithm demonstrates the ability to predict cardiovascular risk by analyzing retinal images with an accuracy rate of 70%. It is only slightly lower than the commonly used blood test method, which has an accuracy rate of 72%.
Alun Hughes, professor of Cardiovascular Physiology and Pharmacology at London’s UCL, said Google’s approach sounded credible because of the “long history of looking at the retina to predict cardiovascular risk.” He also stated that artificial intelligence could potentially speed up existing forms of medical analysis. Further adding, he advised caution that the algorithm would need to be tested further before it could be trusted.
This is likely why Google has launched initiatives such as Project Baseline, a four-year study that aims to compile comprehensive medical records of 10,000 people.
It is believed that through the accumulation of sufficient data, artificial intelligence will be capable of potentially saving countless lives in the future. And the tech developments like Google and Wellnest AI are time-tested proofs of the positive implications of cognitive computing in the field of medicine.
Wellnest’s AI Interpretation and Application
Wellnest Tech has developed an AI using data from millions of people to diagnose cardiovascular diseases. The Clinically certified and compliant AI studies the ECG Report of the patient and interprets it immediately.
The Wellnest 12L app also allows the doctor to notify the patient if they need urgent attention or possible medical care in the future. This way, the tech not only helps with immediate intervention but also allows the doctor to track the patient’s all past records as well for precise interpretation, diagnosis, and treatment.
The healthcare industry is gradually recognizing the significance of integrating AI-powered tools in its next-generation healthcare technology. The potential benefits of AI are immense, and it is believed that AI can streamline and elevate any process within the healthcare operation and delivery system.
It is a common belief that AI tools will replace the work of physicians and other healthcare staff, but rather, they will augment and improve the quality of human work. AI is poised to assist healthcare personnel with a plethora of tasks, ranging from administrative workflows to clinical documentation, patient outreach, and specialized support, such as image analysis, medical device automation, and patient monitoring.
The areas where AI can have the most significant impact are administrative workflows, image analysis, robotic surgery, virtual assistants, and clinical decision support. By leveraging AI, healthcare providers can enhance the quality of patient care and improve overall efficiency in the healthcare ecosystem.