Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review

Abstract

Background Artificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers. Objective This review aimed to systematically examine the literature, in particular, focusing on the performance comparison between advanced AI and human clinicians to provide an up-to-date summary regarding the extent of the application of AI to disease diagnoses. By doing so, this review discussed the relationship between the current advanced AI development and clinicians with respect to disease diagnosis and thus therapeutic development in the long run. Methods We systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered. Results A total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience. Conclusions Current AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians’ experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.

Feasibility of a 5G-Based Robot-Assisted Remote Ultrasound System for Cardiopulmonary Assessment of Patients With Coronavirus Disease 2019

“…With the in-depth application of artificial intelligence in the medical field, integrating artificial intelligence into robot-assisted remote ultrasound systems would greatly increase the scope of use for this technology, facilitate the diagnosis of lung lesions objectively and accurately, and implement automatic switching between probes on the ultrasonic robot system to facilitate optimal imaging of multiple organs and improve image quality. [56][57][58] Interpretation This study showed that the 5G-based robot-assisted remote ultrasound system is a feasible option for safely and effectively performing cardiopulmonary examinations of patients with COVID-19 in isolation wards.…”

Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs

“…These findings are consistent with a recent meta-analysis investigating performance of artificial intelligence versus clinicians in disease diagnosis. 21 TA B L E 6 Performance comparison of accuracy driven model, sensitivity driven model, and board-certified radiologist The application of artificial intelligence and computer-aided diagnosis has recently gained considerable attention in human medicine with an emerging recognition that these technologies will dominate the field of medical imaging diagnosis in the not too distant future. [22][23][24][25][26] By comparison, there is a paucity of veterinary literature and only a few studies were performed since the advent of robust convolutional neural network models currently available.…”

The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

“…The sugarcane plot contains 100 by 100 grid cells. The conditions for identification of the shadow region in the grid cell for both expert and proposed model must have 50% of shadow in the square (thick square) [33]. The example of experiments for shadow detection is shown in Figure 9.…”