Recently AI techniques have sent huge waves across Healthcare chatbots, even fueling an active investigation of whether AI doctors will eventually replace human physicians in the future. We believe that machines in the foreseeable future will not replace human surgeons, but AI can surely assist physicians in making better clinical decisions or even replacing human judgement in specific functional areas of healthcare.
The growing availability of healthcare data and rapid development of big data analytic methods have made possible the recent successful utilization of AI in healthcare. Controlled by relevant clinical questions, powerful AI techniques can unlock clinically significant information hidden in the massive amount of data, which in turn can help clinical decision making.
What Is The Purpose Of AI In Healthcare?
The uses of AI have been extensively discussed in the medical literature. AI can use advanced algorithms to ‘learn’ features from a large volume of healthcare data, and then use the gathered insights to assist clinical practice. It can also be provided with learning and self-correcting abilities to improve its efficiency based on feedback. An AI system can assist physicians by presenting up-to-date medical information from journals, textbooks and clinical studies to inform proper patient care.
Also, an AI system can assist in reducing diagnostic and therapeutic mistakes that are inevitable in human clinical practice. Moreover, an AI system obtains useful information from a large patient population to help to make real-time inferences for health risk alert and health result prediction.
Before AI systems can be extended in healthcare applications, they need to be ‘trained’ through data that are produced from clinical activities, such as screening, treatment assignment, diagnosis and so on, so that they can learn related groups of subjects, associations between subject characteristics and outcomes of interest. These clinical data usually exist in but not limited to the form of demographics, medical notes, electronic records from medical devices, physical examinations and clinical laboratory and images.
Notably, in the diagnosis stage, a substantial proportion of the AI literature investigations data from diagnosis imaging, genetic testing and electrodiagnosis. For example, Jha and Topol urged radiologists to utilize AI technologies when analysing diagnostic images that contain vast data information.
Also, physical examination records and clinical laboratory results are the other two primary data sources. They also distinguish them with genetic, image and electrophysiological (EP) data because they include large portions of unstructured narrative texts, such as clinical notes, that are not instantly analysable.
As a result, the corresponding AI applications focus on first converting the confusing text to machine-understandable electronic medical record (EMR). For example, Karakulah et al. used AI technologies to obtain phenotypic features from case reports to enhance the diagnosis accuracy of congenital anomalies.
Use In Making Decision:
Improving care needs the alignment of big health data with appropriate and timely decisions, and predictive analytics can help clinical decision-making and actions as well as priorities administrative tasks.
Using original recognition to identify patients at risk of developing a condition – or seeing it decline due to lifestyle, environmental, genomic, or other factors – is a different area where AI is beginning to take hold in healthcare. Not only healthcare chatbots there are also Sales Assistant Bot which used to support in showrooms to seal the things.