4 Top Applications of AI in healthcare

Written by: Maliha Safilullah

Maliha Safiullah is a Former Correspondent for 60 Minutes, Channel Nine Australia and is a published writer having worked as a Feature Writer for Dawn News.

As technology advances in leaps and bounds, Artificial Intelligence (AI) has now become the backbone of all ground-breaking innovations founded on various revolutionary concepts that may redefine the functionality and services of several industries once these innovations are put to use.

LocateMotion’s SenSights is an AI Powered Health Intelligence Platform that ingests health data from multiple existing sources and has a predictive model that helps protect a patient’s wellness by identifying risk propensity. SenSights is a high tech solution providing value to patients and reducing hospital readmission rates. SenSights is technologically superior and has a personal emergency response system designed with medical alert systems with fall detection and GPS for active older adults who may be prone to falls or wandering.

The AI program is committed to providing innovative health and wellness and excellence in service with the ability to operate a complex system, modify/adapt a solution, and also assist in data collection, analytics and best remote patient monitoring system.

Though, relatively new, application of AI in healthcare is already providing care management solutions, reforming accumulation of data, medical imaging, radiology, cardiovascular disease diagnosis and hence, minimizing human error. The effects and uses of AI are already being felt in several health care areas:

AI for Smart Health Diagnosis

Accurate and timely diagnosis is the most vital factor in the final result of a patient’s treatment. For the operations of any healthcare facility, people responsible for patient diagnosis, health logistics and radiology are extremely important. Those with professional education in health informatics may play an integral role and can aid in progressing AI further.

AI has presented the most potential in diagnostics with its expert system algorithms that minimizes the time required for diagnosing any illness, be it trivial or serious. The speed at which AI promptly processes information and concludes causes for symptoms can considerably lessen the diagnosis-treatment-recovery cycle for many patients. Furthermore, it diminishes human error and arrives at results based on large data collection and analysis.

SenSights virtual health, remote monitoring AI system serves patients as virtual caregivers that supports older adults and monitors their activities and medication timetables between follow up visits. The patient monitoring system can be ingested with a variety of large data and provides information and solutions that are easy to comprehend and follow through.

AI for Medical Imaging and Radiology

Another application of AI in healthcare is in the understanding of medical imaging of multiple and various body areas. To take this a step further, AI can accumulate data sources from varying times and multiple body zones and after analysis, generate predictions regarding a patient’s health. The application of AI to generate insights for patients with chronic/terminal diseases can aid in treatment decisions and in some cases, an early prediction may provide precautionary care and adherence of an onset of a chronic condition.

For procedures involving radiology or medical imaging, data for handling, processing, and interpretation is plenty and time-consuming but AI capabilities can achieve this by reducing the workload and improving the standardized processes. AI proficiencies in both diagnostic and interventional radiology once developed and employed can facilitate the analysis of large volumes of image data to create meaningful insights that can benefit the entire healthcare system. Healthcare professionals will be in a better position to identify various organ diseases along with other fractures and injuries in a shorter span of time.

As an example, in an article “Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children” published in the Journal of the American Medical Association, [1] the authors describe a machine learning tool developed that swiftly analyzes thousands of images from children’s biopsies and differentiates between environmental enteropathy and celiac disease.  Since these two orders have similar symptoms and at early stages are confused for one another, this application of AI is a solution presented to lessen degrees of stunted growth in children that are a consequence of an untimely or an incorrect diagnosis.

AI for Cardiovascular Diseases

Cardiovascular Disease is determined through clinical data obtained through BMI – a ratio of weight to height and the Framingham Risk Score (FRS) which includes sex, age, blood cholesterol, blood pressure and other related information of a person. Since this methodology is not precise, often people at a potential risk of a heart attack or other related heart condition go undiagnosed or in some cases, those at no risk at all are misdiagnosed. This leads to multiple cases of people spending time, energy and finances on tests, surgeries and other treatment.

AI capability for detection of cardiovascular diseases works with imaging data and analyzes it with data points obtained from various sources. It identifies the risk of heart disease, recognizes early signs and provides preemptive measures before a heart attack. AI developed software, consisting of an advanced algorithm that analyzes the health of the heart by the heartbeat using a neural system expert on numerous datasets of heart sounds. The sound examination works on detecting the even or uneven vibrations of tissue caused by disorderly blood flow, a slight mutter that is undetectable to human hearing since it coincides with the cardiac beat. Since misdiagnosis of cardiovascular disease is a common practice owing to complex symptoms and undetected conditions, AI’s accuracy and speed will ensure that correct recommendation to patients reduces the chances of a misdiagnosis.

With lower misdiagnosis rates and at-risk people being recommended timely and properly, many will be spared the excessive expenditure and time wastage on tests and medications. This way AI will also aid hospitals and healthcare providers by ensuring that their money and medical resources are saved.

AI for Remote Patient Monitoring

As remote patient monitoring systems are growing into AI-powered disease management systems, the opportunity to radically reform healthcare and solve the healthcare cost crisis has presented itself and should be availed.

LocateMotion’s SenSights is an AI analytics platform providing innovative care and comfort beyond conventional modes of healthcare and redefining remote patient monitoring. The device-agnostic design offers an all-in-one experience across many devices and systems without the need for special adaptations. This design accounts for the input method, connection speed, resolution and any other variable/limitation that could be encountered on a device.

Remote monitoring technologies include AI-powered wearable and sensors that use personal and health data from multiple devices to monitor patients and older adults and then use machine learning and other artificial intelligence practices to predict relapses before they happen. The AI algorithms constantly monitor and analyze a multitude of vital signs that include pulse rate, oxygen saturation, respiration rate, temperature, and movement, from not just the wearable device, but the platform can also capture additional metrics from other devices.

These intuitive algorithms and a customizable platform supports the individual requirements of a patient. Caregivers or healthcare staff can use virtual visits to assess and treat patients through an in-built interface intended to support the process of monitoring, managing and intervening in the health of patients at home.