Use Cases of AI in Healthcare

The different types of AI technology in healthcare have their own purpose benefiting the industry.

• NLP: NLP stands for Natural Language Processing. Its use cases are the analysis of texts, voice-to-text, voice recognition, etc. In the healthcare sector, NLP helps in collecting and categorizing research and clinical documentation. Unorganized clinical notes on patients may be analyzed by NLP systems, providing a wealth of information for improved patient care and technique improvement.
• Machine Learning: One of the popular applications of AI in the healthcare sector is machine learning (ML). The use of machine learning steadily increases accuracy by duplicating human learning through data and algorithms. One of the most amazing uses of machine learning in healthcare is precise medicine. Based on an array of patient characteristics and therapies, it forecasts which treatment methods will work best for the patients. Most applications in precision healthcare use supervised learning with a training dataset that has predetermined results.
• Rule-based systems: The most basic type of AI is a rule-based expert system, which solves problems by applying predetermined knowledge-based rules. The expert system’s task is to take human expert knowledge and turn it into a number of hardcoded rules that can be applied to the incoming data. They are commonly used in the medical field to support clinical decisions. However, when the number of regulations rises, they start to clash and become unworkable.
• RPA: Robotic Process Automation, or RPA, automates repetitive operations with software bots to free up human time for more complicated work, increasing corporate productivity. They assist in automating time-consuming processes like billing or patient record updates.

Medical Imaging Enhancement

In a CT scan or echocardiography, even the most skilled doctor might overlook a little detail. It is quite common for a doctor to miss symptoms of unrelated illnesses, particularly if they are concentrating on looking for information on a medical imaging report linked to a particular problem.

Furthermore, doctors may diagnose tumors, liver and kidney infections, increase the outlook for cancer, and more with the use of image recognition.

Virtual Nurse

Managing large patient numbers presents many problems for healthcare organizations looking to maintain low costs while enhancing patient outcomes. Nurses frequently have to care for too many patients at once, so any technology that might lessen their labor is appreciated.

Virtual nurses represent an amalgamation of cutting-edge technology, ranging from computer vision, natural language processing (NLP), and sophisticated robotics, to machine learning (ML) for analyzing numerous patients.

Fraud Reduction

AI is employed in detecting fraudulent activities in healthcare claims, ensuring that resources are allocated efficiently. Moreover, AI aids in managing healthcare systems by optimizing workflows, resource allocation, and patient care coordination. AI in healthcare not only identifies insurance fraud but also guards against patient data theft.

Mental Health Apps

AI-driven mental health apps and chatbots are providing support for individuals dealing with mental health issues. Woebot, for instance, uses natural language processing to engage with users, offering therapeutic conversations and coping strategies.

Remote Patient Monitoring

AI facilitates remote monitoring of patients, especially those with chronic conditions. Wearable devices equipped with AI algorithms can track vital signs, detect abnormalities, and alert healthcare providers in real time. This allows for early intervention and reduces the need for frequent hospital visits.

Predictive Analytics

By analyzing patient data and historical trends, AI can predict disease outbreaks, patient admissions, and potential complications. This proactive approach enables healthcare providers to allocate resources effectively and implement preventive measures.