The healthcare industry is rapidly evolving, and the use of artificial intelligence (AI) and machine learning (ML) is becoming increasingly common. AI and ML are being used to detect diseases more quickly, provide personalized treatment plans, automate certain processes, such as drug discovery or diagnosis, and improve patient outcomes. AI is also being used to make healthcare more predictive and proactive by analyzing big data to develop better preventive care recommendations for patients. AI and ML are also being used for medical diagnostic purposes, such as recognizing potentially cancerous lesions on radiological images.
Statistical NLP is based on machine learning (in particular, deep learning neural networks) and has contributed to a recent increase in recognition accuracy. A more complex form of machine learning is the neural network, a technology that has been available since the 1960s and has been well established in healthcare research for several decades. Neural networks have been used for categorization applications, such as determining if a patient will contract a particular disease. Thanks to recent advances in computing and informatics, artificial intelligence (AI) is rapidly becoming an integral part of modern healthcare.
AI can help doctors and medical providers provide more accurate diagnoses and treatment plans, as well as help make healthcare more predictive and proactive by analyzing big data to develop better preventive care recommendations for patients. AI also has the potential to save lives and money by improving the health system. Perhaps the only healthcare providers who will lose their jobs over time are those who refuse to work alongside artificial intelligence. Given the extraordinary impact that improvements in the health system can have for many people, healthcare has become a key sector for investment and efforts in artificial intelligence and machine learning.
AI and ML in the health sector will continue to improve and have an impact on disease prevention and diagnosis, will make more use of data from several clinical trials, help develop personalized drugs based on a person's unique DNA, and will inform treatment options.
