The Future of AI in Healthcare

The future of AI in healthcare is exciting. As AI-powered applications become more sophisticated, they will introduce various changes in how healthcare is delivered. There will be a shift from reactive to proactive care, emphasizing preventive care and early intervention.

AI will also change the way healthcare practitioners interact with patients. Instead of one-size-fits-all care, AI will allow them to deliver personalized care tailored to each patient. This will result in better health outcomes and a more efficient healthcare system.

Healthcare providers are just beginning to scratch the surface of what AI can do. As more sophisticated AI-powered applications are developed, even more transformative changes in healthcare will surface. AI’s potential is limitless.

Neural Networks & Modern Deep Learning

Supervised learning is the most common type of Machine Learning in a clinical setting. Supervised learning uses the physical traits of the patient, backed with a database of information (in this case, breast cancer genes), to provide a more targeted outcome. Another type of learning used is Modern Deep Learning, considered to go beyond the surface of Machine Learning.

Deep Learning takes the same inputs as Machine Learning but feeds them into a computerized neural network, a hidden layer that further files the information to a more simplified output. This helps practitioners with multiple possible diagnoses to narrow down to one or two outcomes, thus, allowing the practitioner to make a more definitive and concrete conclusion.

Natural Language Processing (NLP)

Natural Language Processing is similar to structured data processes, which focus on all unstructured data in a clinical setting. This data is from clinical notes and documented speech-to-text processing when a practitioner sees a patient. This data includes narratives from physical examinations, laboratory reports, and exam summaries.

Natural Language Processing uses historical databases with disease-relevant keywords aiding in a diagnosis's decision-making process. Using these processes can provide a more accurate and efficient diagnosis for a patient, which in turn saves time for the practitioner, and, more importantly, can speed up the treatment process. The faster, more targeted, and more specific the diagnosis, the sooner a patient can be on the road to recovery.