AI and Healthcare-Machine Learning Case Studies

The following examples of Machine Learning apps for healthcare uses demonstrate the current application of AI in the healthcare industry.

1-SmokeBeat is an innovative application that passively gathers data on the user’s smoking behavior. The application uses an accelerometer on a smartwatch or a smart band to detect hand-to-mouth gestures. Additionally, SmokeBeat compares users' smoking data with their peers of choice, creating a sort of supportive social network.

?2-Virtual Nursing-Virtual nurses are computer-generated avatars that can interact with patients like humans. They are designed to be social, empathic, and informative, Virtual nurses can interact with patients more regularly than human nurses and answer questions in-between doctor visits. They offer quick answers (faster than waiting for a nurse) and they are available 24/7. One example of a virtual nurse is Molly. This is a female avatar able to remotely monitor medical conditions, which would be challenging to monitor on the spot. Molly receives data such as blood pressure and weight from monitoring devices connected via Bluetooth. These devices are positioned in patients' homes, which makes it convenient to take measurements as often as needed.

?3-Medical Imaging-Even with all the advancements in technology, medical image analysis is a tedious task prone to human error since itrequires great attention to detail. With the help of machine learning, it's possible to detect even the subtlest changes in medical scans. Furthermore, traditional scan analyses (such as CAT scans and MRI) are time-consuming. SubtleMR developed by Subtle Medical is a machine learning-based software solution that improves the quality of MRI protocols. With the help of denoising and resolution enhancement, SubtleMR can improve image quality and sharpness with any MRI scanner and field strength. For example, RadNet, a US leader in outpatient imaging with 335 centers across the country, accelerated its protocols by 33-45% after adopting SubtleMR technology.

4-Robot Assisted Surgery-robotic assistance in surgery increases precision, allows access to different areas of the human body with minimal penetration and alleviates pressure from human surgeons as robots can take over some parts of the work. Senhance Surgical system is a console-based, multiarmed surgical system that allows surgeons to remotely control it. The system heavily relies on machine learning and deep learning models to bring the most challenging healthcare ideas to reality. For example, during the preoperative stage, a machine learning-driven database allows surgeons to go through simulation training. During surgeries, based on data from the eye-tracking camera, the system's Intelligent Surgical Unit can automatically adjust the camera view and predict when a surgeon needs to zoom in or enhance images in real-time.??

5-Disease Outbreak Prediction-a huge amount of data can be collected from satellites. This includes real-time data from social media and other historical web data. Machine learning algorithms help aggregate this data and make predictions about potential disease outbreaks. ProMED (the Program for Monitoring Emerging Diseases) offers an online real-time reporting system showing outbreaks of infectious diseases worldwide and any exposure to toxins affecting human or animal health. ProMED aggregates data from sources such as official reports, media reports, local observers, and reports contributed by its subscribers. An expert team reviews these reports before they are accepted into the system.

6-Medical Diagnostics-In healthcare, inaccurate or incomplete diagnosis of diseases can be detrimental to patient outcomes, and, in the worst-case scenarios, lead to death. To address one of the most apparent healthcare challenges, many companies are tapping into machine learning to make medical diagnostics more accurate. A great example is the Face2Gene app, a machine learning-enabled facial recognition software that helps clinicians to more accurately diagnose rare diseases. With the help of machine learning, Face2Gene can detect phenotypes, reveal relevant facial features, and evaluate the probability of a patient having a particular syndrome.

?Other Companies using Machine Learning in healthcare applications are:

1-Deep Genomics-The Deep Genomics’ artificial intelligence-powered platform accelerates research by helping healthcare professionals quickly find candidates for the development of drugs for specific disorders.

2-Intuitive Surgical-Intuitive Surgical are the developers of the most widely used machine learning-powered surgical system called Da Vinci. Da Vinci Surgical System allows surgeons to perform robotic-assisted, minimally invasive surgeries that significantly improve surgery outcomes.

3-PathAI-PathAI uses machine learning to help pathologists to make more informed diagnostic decisions. PathAI works with renowned drug developers and healthcare organizations to extend the reach of artificial intelligence and machine learning in healthcare.

AI applications offer great potential for healthcare optimization, however, they can become less effective for the following reasons:

1-Lack of Data-Given that datasets from one organization rarely suffice for model training, engineers typically resort to obtaining patient data from other healthcare organizations. The problem is that the majority of these datasets are incompatible with each other.

2-Bias-Since it's humans who train machine learning algorithms, our existing biases inevitably creep in. What’s even more daunting is that ML models do not only sustain these prejudices but often amplify them.?

3-Lack of Strategy-Given that machine learning has a much more drastic impact on conventional healthcare workflows than the majority of other technologies, companies should make an effort to redefine team roles, invest in change management, and launch workforce reskilling programs.?

4-Lack of In-house Expertise-On the one hand, many ambitious AI startups fail to incorporate clinical expertise during the early phases of development, while, on the other hand, many credible and experienced clinicians have insufficient understanding of machine learning to provide tangible input.??

Ed Cardon

EPC Search International LLC

470-345-0846

[email protected]