Artificial intelligence is increasingly being used in healthcare, and its potential presents many opportunities for exploitation. According to a report by Grand View Research, the global market for AI in medicine will grow at an average annual rate of nearly 42% between 2021 and 2028. In addition, the increasing amount of digital data, the pressure to reduce health care spending, and the need to diagnose patients quickly, is intensifying the need to automate the above activities. The implementation of artificial intelligence into healthcare facilities would make it possible to process incoming data, identify patterns and make decisions with minimal staff involvement. In addition to streamlining activities related to the overall operation and management of the institution, AI also significantly impacts the development of medicine. Among other things, AI is improving the speed and accuracy of diagnoses and screening for various diseases.
Application of AI in healthcare
Artificial intelligence in healthcare can support many processes from an organizational, but also from a medical perspective. Here are some examples of the use of artificial intelligence in healthcare facilities:
– clinical operations – AI makes it possible to recognize patterns of health complications that are recorded. This allows more accurate diagnoses to be made. In addition, clinicians conducting clinical trials can use AI to speed up medical coding searches and approvals in the new technology area,
– Analyzing medical imaging – Artificial intelligence enables radiologists or cardiologists to identify important problems and critical health cases. This allows them to prioritize, focusing their attention on critical cases first. In addition, the algorithm can also analyze data sets at high speed, comparing them with other studies to identify patterns,
– drug development process – artificial intelligence, based on databases of molecular structures, makes it possible to verify which drugs could be effective for specific diseases. Using spliced neural networks, it is able to predict the binding of small molecules to proteins, while analyzing clues from millions of experimental measurements and thousands of protein structures,
– analyzing unstructured data – health data and patient medical records are very often stored in an unstructured manner. Artificial intelligence can search, collect, store and standardize medical data,
– Cancer treatment and diagnosis – Artificial intelligence (AI) helps doctors make informed decisions about cancer, mainly in treatment with radiation therapy. By collecting relevant patient medical data, AI can assess the quality of care provided, optimize treatment and provide accurate results based on oncology imaging,
– Predictive analytics – using AI to perform predictive analytics, allows for greater efficiency in workflow, medical decision-making and treatment plan. Neuro-linguistic programming and Machine Learning reads a patient’s entire medical history in real time, linking it to symptoms, chronic ailments or illnesses affecting other family members. AI algorithms are able to turn the result into a predictive analysis tool that can detect and treat the disease before it threatens the patient’s life,
– discovery and development of genetic medicine – artificial intelligence makes it possible to predict the likelihood of genetic diseases. This is possible by collecting data on identified compounds and biomarkers relevant to certain clinical trials.
Benefits of AI in healthcare
– Faster and more accurate inference – using AI and machine learning-based solutions, healthcare organizations can draw faster and more accurate conclusions from large data sets. This translates into more accurate diagnosis of patients and increased satisfaction levels in both employees and patients,
– saving time – artificial intelligence, thanks to its ability to deal with large amounts of data, allows quick access to comprehensive electronic patient medical records or the latest treatment guidelines for specific conditions. As a result, doctors don’t have to waste valuable time on lengthy reviews of records, but can instead focus on treating and diagnosing patients,
– Easier clinical decision-making – doctors in many cases need to act quickly and, above all, precisely. By using machine learning, they can more easily make clinical decisions by implementing specific steps in treating patients,
– More accurate disease detection – AI very accurately detects many diseases, including cancers, even in the early stages. For example, the use of AI makes it possible to review and translate mammograms 30 times faster with 99% accuracy, reducing the need for unnecessary biopsies.
Source:
www.ibm.com.pl
www.healthcareweekly.com
www.mobihealthnews.com