Top 7 AI Examples In Healthcare - The Medical Futurist

Artificial intelligence is no longer a futuristic idea. It's already here, and it has turned out to be a powerful, disruptive force in healthcare fueling some of the most innovative diagnostic tools of today. This article looks at seven examples where AI has started to transform healthcare, showing how it helps both patients and medical professionals.

AI's Impact on Patient Care

Detecting Arrhythmias

Atrial fibrillation (AFib) can increase the risk of stroke and heart failure. Dealing with it used to be hard because it needed continuous ECG monitoring. Now, AI has changed that. Digital health devices, like the AliveCor Kardia, are FDA-approved ECG recorders. The Kardia algorithm works in the background, analyzing readings on the go. This means patients can be monitored anywhere, not just in hospitals.

Emergency Detection

AI can make patient pathways better and even help nurses. HCA Healthcare made a predictive algorithm called Sepsis Prediction and Optimization of Therapy. It watches patient data in hospitals to find possible sepsis cases. This algorithm can find sepsis six hours earlier than doctors, which has helped cut sepsis mortality rates by almost 30 percent.

Seizure-Detecting Smart Bracelets

Epilepsy is a common neurological problem. Wearable devices, like the Empatica Embrace AI-powered wristband, are made to tell users and caregivers about a developing seizure. Clinical tests of Embrace showed a 98% accuracy rate for finding generalized tonic-clonic seizures. The goal is to be able to predict seizures in the future.

Skin Checking Apps

Skin checking apps let users take pictures of suspicious skin lesions, upload them, and have an AI algorithm look at them. These algorithms compare user images to large databases and give a first diagnosis in seconds. While dermatologists are still needed for a confirmed diagnosis, these apps are very accurate and can find skin lesions that might have been missed by traditional healthcare methods.

Stroke Detection

The Viz LVO app uses a deep learning algorithm to automatically find stroke on CT images and tell stroke specialists about patients who might be treatable. Stroke is a very time-sensitive condition. This system helps by cutting down the time it takes for patients to get care. In a study, Viz LVO had 96% sensitivity and 94% specificity. Faster triage with this app means more patients can be found and treated, which helps patients get better and lowers the chance of long-term problems.

Breast Cancer Detection

Breast cancer is a common cancer diagnosis for women. Deep learning models that look for early signs of the disease have been around for a while. Studies show that combining deep learning systems' predictions with a human pathologist's diagnosis helps patients and eases some of the pressure on radiologists when they make important decisions for their patients.

AI's Role in Drug Discovery

Advanced algorithms could change how drugs are designed. AI could make drug development cheaper and faster by shortening the production cycle and helping the pharmaceutical industry find new drugs without long clinical trials and high costs. Some estimates say it takes about 12 years and almost 3 billion dollars for one experimental drug to go from idea to market. Recently, an advanced machine learning program from Alphabet's DeepMind found 200 million proteins. This could speed up the search for new medicines. This shows the big difference AI can make.

Key Takeaways

• AI is already changing healthcare with practical applications.

• It helps in early detection of serious conditions like AFib, sepsis, and stroke.

• AI-powered tools improve diagnostic accuracy and speed.

• Wearable technology is making personal health monitoring more effective.

• AI has the potential to make drug discovery faster and more affordable.

• These advancements help both patients and healthcare professionals by improving outcomes and reducing burdens.

These are just a few examples of how artificial intelligence can help healthcare professionals. There is still a lot to do to figure out how to use these algorithms more, not just from a technology point of view, but also with rules and regulations. But it's clear that AI is a big change for medical professionals and patients alike, and it's only just beginning.