by Simon Gao, March 6, 2022
The application of AI in telehealth to allow doctors to make real-time, data-driven choices is a key component in generating a better patient experience and improved health outcomes. Besides some obvious and proven benefits of AI based telehealth, such as comprehensive patient information intake, and timely need-based scheduling, reducing doctor workloads, patient care triage recommendations, etc. one important benefit is becoming increasingly notable: remote diagnostics.
Telehealth platforms usually ask patients to input their condition to the system, this would take more information than what doctors would get from face-to-face consultation. Using natural language process (NLP) modeling, more information can be extracted for diagnosis. Combing this information with patient claims data, EMR data, or even wearable activity tracking information, AI can produce a diagnosis that is no less better than a traditional office visit can render.
Years of medical training are important to accurately diagnose diseases. Yet still, diagnostics may be a lengthy and time-consuming procedure. The demand for expertise in many disciplines considerably outnumbers the available supply. This puts professionals under a lot of pressure, and it often causes life-saving patient diagnoses to be delayed. Machine Learning algorithms, especially Deep Learning algorithms, have lately made significant progress in autonomously identifying illnesses, lowering the cost and increasing the accessibility of diagnostics. AI algorithms can learn to recognize patterns in the same way that physicians do. Because there is so much more data available now in many circumstances, algorithms have become as good as professionals at diagnosing, in more situations than ever, AI can now do even better. Examples are:
Using electrocardiograms and cardiac MRI scans to identify the risk.
· Assessing gait for disease propensity
· Finding diabetic retinopathy signs in eye photographs.
· Identifying skin lesions in skin photos and classifying them
For MRI scans, the difference is that the algorithm can arrive at results in a fraction of a second and it can be easily replicated anywhere. Soon, everyone and anywhere will have access to the same high-quality radiological tests from top experts at a reasonable cost.
AI diagnostics that are more sophisticated are on the way, more ambitious systems combine many data sources (CT, genomes, MRI and proteomics, even handwritten patient files) to diagnose a disease or its cause. In genetics-based test, there are companies studying pictures of patients to build an AI system to identify the presence of uncommon genetic disorders. Patients with uncommon genetic diseases may need an average of several visits to the doctor’s office before a proper diagnosis can be made. The number of visits may be lowered to zero using AI and telehealth. Simply transmit a picture of the patient’s face to the clinician, and the AI system will evaluate it and accurately identify the condition.
Doctors and patients may anticipate diagnosis to become a more time and cost-effective procedure as a result of the simplicity of identification made possible by using AI in telehealth.
