ISO/TR 24291:2021 pdf download – Health informatics — Applications of machine learning technologies in imaging and other medical applications

02-13-2022 comment

ISO/TR 24291:2021 pdf download – Health informatics — Applications of machine learning technologies in imaging and other medical applications.
5.1.2 Robotics In robotics, AI can provide high quality treatments by increasing the precision and accuracy of the surgical process. For example, it can control the trajectory, depth, and speed of the robot movements with high precision and can go where traditional tools cannot. It can also reduce the burdens of the surgeons during surgery by providing the same, repetitive movements without fatigue. 5.1.3 Continuous monitoring Proper treatment within golden time could be performed by continuously monitoring of patient condition and alerting nurses by AI. AI model with continuous monitoring data also can alert the clinicians before onset. 5.1.4 Machine learning By using traditional machine learning methods, AI can be used to predict response by analysing data affecting treatment outcomes. 5.1.5 Deep learning By using deep learning, self-learning ability to process large amounts of imaging and audios records in medicine, reducing uncertainty in medical treatment decisions including computer aided detection, computer aided diagnosis, computer aided differential diagnosis, and clinical decision support system. Deep learning can handle multiple different types of clinical data such as images, texts, and signals at the same time. 5.1.6 Image processing In image processing, AI can be used to process large-scale medical images and apply them to detect diseases, diagnosis, etc. AI for clinical image handling has demonstrated its performance in clinical settings. 5.1.7 Natural language processing In NLP, AI can be used to translate long descriptive character sets such as electric medical records to be interpreted, i.e. extracting the information from unstructured electronic medical records.
5.3.2 Clinical trials In clinical trials, automated case selection could be one of important use cases by using AI-based search techniques to help find the right disease and patient, reduce the time to prepare for clinical trials, and improve objectivity. 5.3.3 Clinical assistance In clinical assistance, there are two sub-categories including assistant service and wellness with IoT and automated dictation. In assistant service and wellness with IoT, convergence of IoT technology, voice recognition technology and artificial intelligence technology, efficient reservation, diagnosis and medical treatment process, business information update and customized curation could be one of important use cases. In automated dictation, speech recognition and document generation technology can automate diagnosis and recording of readings and read and structure medical terminology.

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