Application and Development of Intelligent Medicine in Traditional Chinese Medicine

As modern science and technology constantly progresses,the fields of artificial intelligence,mixed reality technology,remote technology,etc.have rapidly developed.Meanwhile,these technologies have been gradually applied to the medical field,leading to the development of intelligent medicine.What’s more,intelligent medicine has greatly promoted the development of traditional Chinese medicine(TCM),causing huge changes in the diagnosis of TCM ailments,remote treatment,teaching,etc.Therefore,there are both opportunities and challenges for inheriting and developing TCM.Herein,the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed,with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.

The critical need to establish standards for data quality in intelligent medicine

Medical artificial intelligence(AI)is an important technical asset to support medical supply-side reforms and national development in the big data era.Clinical data from multiple disciplines represent building blocks for the development and application of AI-aided diagnostic and treatment systems based on medical big data.However,the inconsistent quality of these data resources in AI research leads to waste and inefficiencies.Therefore,it is crucial that the field formulatesthe requirements and content related to data processing as part of the development of intelligent medicine.To promote medical AI research worldwide,the“Belt and Road”International Ophthalmic Artificial Intelligence Research and Development Alliance will establish a series of expert recommendations for data quality in intelligent medicine.

Expert recommendations on collection and annotation of otoscopy images for intelligent medicine

Middle and outer ear diseases are common otological diseases worldwide.Otoscopy and otoendoscopy exami-nations are essential first steps in the evaluation of patients with otological diseases.Misdiagnosis often occurs when the doctor lacks experience in interpreting the results of otoscopy or otoendoscopy,leading to delays in treatment or complications.Using deep learning to process otoscopy images and developing otoscopic artificial-intelligence-based decision-making systems will become a significant trend in the future.However,the uneven quality of otoscopy images is among the major obstacles to development of such artificial intelligence systems,and no standardized process for data acquisition,and annotation of otoscopy images in intelligent medicine has yet been fully established.The standards for data storage and data management are unified with those of other specialties and are introduced in detail here.This expert recommendation criterion improved and standardized the collection and annotation procedures for otoscopy images and fills the current gap in otologic intelligent medicine;it would thus lay a solid foundation for the standardized collection,storage,and annotation of oto-scopy images and the application of training algorithms,and promote the development of automatic diagnosis and treatment for otological diseases.The full text introduced image collection(including patient preparation,equipment standards,and image storage),image annotation standards,and quality control.

Hunting down the dominating subclone of cancer stem cells as a potential new therapeutic target in multiple myeloma: An artificial intelligence perspective

The development of single-cell subclones,which can rapidly switch from dormant to dominant subclones,occur in the natural pathophysiology of multiple myeloma(MM)but is often"pressed"by the standard treatment of MM.These emerging subclones present a challenge,providing reservoirs for chemoresistant mutations.Technological advancement is required to track MM subclonal changes,as understanding MM's mechanism of evolution at the cellular level can prompt the development of new targeted ways of treating this disease.Current methods to study the evolution of subclones in MM rely on technologies capable of phenotypically and genotypically characterizing plasma cells,which include immunohistochemistry,flow cytometry,or cytogenetics.Still,all of these technologies may be limited by the sensitivity for picking up rare events.In contrast,more incisive methods such as RNA sequencing,comparative genomic hybridization,or whole-genome sequencing are not yet commonly used in clinical practice.Here we introduce the epidemiological diagnosis and prognosis of MM and review current methods for evaluating MM subclone evolution,such as minimal residual disease/multiparametric flow cytometry/next-generation sequencing,and their respective advantages and disadvantages.In addition,we propose our new single-cell method of evaluation to understand MM's mechanism of evolution at the molecular and cellular level and to prompt the development of new targeted ways of treating this disease,which has a broad prospect.

Expert recommendation on collection,storage,annotation,and management of data related to medical artificial intelligence

Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform criteria for medical data quality have yet to be established.Therefore,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical AI.This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.