Artificial intelligence is slowly becoming a key part of our life, whether it be incorporated in business or in our everyday life. Considering that, artificial intelligence has found its way to be included in the healthcare system, with the potential to not only help in repetitive administrative issues in the field, but also put insight into diagnostics and patient care. Healthcare providers do not need to be afraid of artificial intelligence replacing them yet. Even with the accelerated development and new findings, including some cases that it's assumed that AI can perform as well or even better than humans in certain diagnostics aspects, the technologies have not developed deep enough. Reasons such as ethical ones, trials that need to last a longer amount of time to ensure the efficacy of the technology and so on, have made it sure that it will take years before these technologies are fully incorporated into the real healthcare world. Artificial intelligence forms in the healthcare system AI has been slowly getting into the healthcare field for the past half century. The more modern form of artificial intelligence which is currently also the most common is machine learning, with its main focus being the potential effect in accuracy of treatment protocols and health outcomes through algorithmic processes. Machine learning also includes different types of learning which can affect the way it’s applied into healthcare. - Natural Language Processing, which has been a form that has been developing for over 50 years and is still relevant. The systems include forms of speech recognition or text analysis and translation. - Rule-based Expert Systems is the simplest form of artificial intelligence, which includes systems based on variations of ‘if-then’ rules. It is used in clinical decision support even to modern days, but these rule-based systems are slowly being replaced by Machine Learning systems.
Medical field professionals will need to learn how to work with all types of technologies incoming, as generally all technological advances in the field have only made diagnostics and patient care significantly easier.
Machine learning So what is machine learning? Explained more simply, machine learning is essentially a subset of artificial intelligence which takes past data and information, learns and creates patterns based on those without programming explicitly. To not directly jump into diagnostics and patient care, machine learning as a basis can help in record keeping and data integrity. Recordkeeping especially when it comes to electronic health records (EHRs) could optimize operations and make it easier for healthcare providers to access the information needed, especially with automating image analysis and providing clinical decision support. Deep learning Deep learning is a very complex part of machine learning that imitates the way the human brain functions and is used in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning has found its way into the medical field through mainly detection of images in radiology. Images are presented and through different neural networks that can learn from data without need of supervision, deep learning apps have managed to be able to detect abnormalities and cancerous lesions in medical imaging. Diagnostics and patient care have the main focus when it comes to AI, mostly because it’s a huge game changer. The technological aspects of developing AI in this field require a different approach which is why it’s more complex. Administrative applications of the field are not as big of a game changer as diagnostics as patient care, considering that administrative issues can be solved by artificial intelligence in other businesses and corporate worlds. Even though not game-changers, AI being applied in administrative issues in healthcare would change the workflow, giving priority to patient care rather than focus on other tasks that take time away, at the same time to be able to reduce healthcare and administrative costs.
