IMPORTANCE OF AI & MACHINE LEARNING IN HEALTHCARE SYSTEM AND MEDICAL IMAGING
INTRODUCTION
The radiology department is said to be the backbone of a hospital, and machines are the roots of the radiology department.
Not only in radiology department but the complete hospital is directly or indirectly relay on machinery. Hence machines and its role in health care is dynamic.
Artificial intelligence (AI) & MACHINE LEARNING (ML) in healthcare
AI, In the simplest sense, AI is when computers and other machines mimic human cognition, and are capable of learning, thinking, and making decisions or taking actions.
ML Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior.
Application of Artificial intelligence (AI) & MACHINE LEARNING (ML) in healthcare
Improving diagnosis
AI & ML can develop better diagnostic tools to analyze medical images. For example, a machine learning algorithm can be used in medical imaging (such as X-rays or MRI scans) using pattern recognition to look for patterns that indicate a particular disease. This could potentially help doctors make quicker, more accurate diagnoses.
Developing new treatments
Can also be used to identify relevant information in data that could lead to the development of new drugs and new treatments for diseases. For example, machine learning could be used to analyze data from clinical trials to find previously unknown side-effects of drugs. This could help to improve patient care and the safety and effectiveness of medical procedures.
Reducing costs
AI & ML can be used to improve the efficiency of healthcare, which could lead to cost savings. For example, machine learning in healthcare could be used to develop better algorithms for managing patient records or scheduling appointments. This could potentially help to reduce the amount of time and resources that are wasted on repetitive tasks in the healthcare system.
Improving care
Can also be used to improve the quality of patient care. For example, algorithms could be used to develop systems that proactively monitor patients and provide alerts to medical devices or electronic health records when there are changes in their condition. This could help to ensure that patients receive the right patient care at the right time.
Higher Productivity with Automation
AI can help analyze medical images faster as it can improve the speed, efficiency and accuracy which can further lead to better care.
Computing Quantitative Data
AI can use quantitative data in multiple ways as human cognition.
It can predict if a patient will suffer from heart failure based on their rate of hospital visits and medical history.
Learning in the field radiology & imaging technology
Radiation, medical imaging, medical diagnostics, are highly relay on the note that well-annotated large data. The computer hardware and software are updating constantly with the modern era.
It is the well-known fact that in the future, machine & AI and its application in radiology is expected to have a substantial clinical impact along with the radiographic imaging examinations
The clinical impact can also lead to have the computers in the routine clinical practice that can also may allow radiology workers to further integrate their knowledge and share it with other medical specialties also and this may allow the precision in medicine field.
The complex structure of many machine learning methods signify much need for continued research and development before full clinical incorporation and use.
Learning of machine is a powerful technique that can applied in rendering medical diagnoses, it can be misapplied as well hence proper knowledge in machines is mandatory.
Machine learning has a greater influence in the future of radiological medical imaging as well as in healthcare sectors.
Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in radiology in India: A survey
