Natural language processing in medicine: A review

Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP’s recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP’s open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities. 

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Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Radiological reports are a valuable source of information used to guide clinical care and support research. Organizing and managing this content, however, frequently requires several manual curations because of the more common unstructured nature of the reports. However, manual review of these reports for clinical knowledge extraction is costly and time-consuming. Natural language processing (NLP) is a set of methods developed to extract structured meaning from a body of text and can be used to optimize the workflow of health care professionals. Specifically, NLP methods can help radiologists as decision support systems and improve the management of patients' medical data. In this study, we highlight the opportunities offered by NLP in the field of radiology. A comprehensive review of the most commonly used NLP methods to extract information from radiological reports and the development of tools to improve radiological workflow using this information is presented. Finally, we review the important limitations of these tools and discuss the relevant observations and trends in the application of NLP to radiology that could benefit the field in the future. 

Natural Language Processing in Radiology: Update on Clinical Applications.

While radiologists can describe a fracture's morphology and complexity with ease, the translation into classification systems such as the Arbeitsgemeinschaft Osteosynthesefragen (AO) Fracture and Dislocation Classification Compendium is more challenging. We tested the performance of generic chatbots and chatbots aware of specific knowledge of the AO classification provided by a vector-index and compared it to human readers. In the 100 radiological reports we created based on random AO codes, chatbots provided AO codes significantly faster than humans (mean 3.2 s per case vs. 50 s per case, p < .001) though not reaching human performance (max. chatbot performance of 86% correct full AO codes vs. 95% in human readers). In general, chatbots based on GPT 4 outperformed the ones based on GPT 3.5-Turbo. Further, we found that providing specific knowledge substantially enhances the chatbot's performance and consistency as the context-aware chatbot based on GPT 4 provided 71% consistent correct full AO codes for the compared to the 2% consistent correct full AO codes for the generic ChatGPT 4. This provides evidence, that refining and providing specific context to ChatGPT will be the next essential step in harnessing its power. 

Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports

The realization of full automation in 3D Concrete Printing (3DCP) encounters numerous obstacles, notably the absence of automated and inline testing methodologies to measure the fresh properties at the construction site. To address this challenge, this study presents an automated device with multifunctional capabilities of conducting penetration, compression, shear, and tensile tests in inline mode. The developed system is integrated with a 3D printer and controlled from the main station. To validate the instrument performance, it was used for inline penetromtery testing to measure the structural build-up of printed layers, and its results were correlated with the conventional compressive and shear strength tests. Additionally, the responsiveness of the instrument to the material variation was validated. This research represents a substantial advancement for in-situ characterization of structural build-up and quality control, while also showcasing its potential utility in mix design development. 

Towards full automation in 3D concrete printing construction: Development of an automated and inline test method for in-situ assessment of structural build-up and quality of concrete

Text-search software can be used to identify people at risk of re-fracture. The software studied identified a threefold higher number of people with fractures compared with conventional case finding. Automated software could assist fracture liaison services to identify more people at risk than traditional case finding. Fracture liaison services address the post-fracture treatment gap in osteoporosis (OP). Natural language processing (NLP) is able to identify previously unrecognized patients by screening large volumes of radiology reports. The aim of this study was to compare an NLP software tool, XRAIT (X-Ray Artificial Intelligence Tool), with a traditional fracture liaison service at its development site (Prince of Wales Hospital [POWH], Sydney) and externally validate it in an adjudicated cohort from the Dubbo Osteoporosis Epidemiology Study (DOES). XRAIT searches radiology reports for fracture-related terms. At the development site (POWH), XRAIT and a blinded fracture liaison clinician (FLC) reviewed 5,089 reports and 224 presentations, respectively, of people 50 years or over during a simultaneous 3-month period. In the external cohort of DOES, XRAIT was used without modification to analyse digitally readable radiology reports (n = 327) to calculate its sensitivity and specificity. XRAIT flagged 433 fractures after searching 5,089 reports (421 true fractures, positive predictive value of 97%). It identified more than a threefold higher number of fractures (421 fractures/339 individuals) compared with manual case finding (98 individuals). Unadjusted for the local reporting style in an external cohort (DOES), XRAIT had a sensitivity of 70% and specificity of 92%. XRAIT identifies significantly more clinically significant fractures than manual case finding. High specificity in an untrained cohort suggests that it could be used at other sites. Automated methods of fracture identification may assist fracture liaison services so that limited resources can be spent on treatment rather than case finding.

Natural language processing of radiology reports for the identification of patients with fracture

Abstract Fracture liaison services (FLS) address the treatment gap for those with osteoporosis (OP) who fracture and are not treated. Given the limited human resources in FLS, screening high volumes of radiology reports for fractures with Natural Language Processing (NLP) could identify patients that have not been recognized or treated. This study is an analytical and clinical validation of X-Ray Artificial Intelligence Tool software (XRAIT) at its development site (a tertiary hospital) and external validation in an adjudicated cohort from the Dubbo Osteoporosis Epidemiology Study (DOES).Methods: XRAIT uses NLP to perform a Boolean search of radiology reports for fracture and related terms. It can be trained for site-specific reporting styles and use rules to refine identification (e.g. age>50y; bone involved; etc). At the development site, XRAIT was used to search the emergency patient presentations of people over 50 years of age and compared to referrals to FLS (usual care) during the same 3-month period. XRAIT analyzed all plain radiographs and CT scans (n = 5089) while n = 224 were referred to FLS for usual care. External validation: XRAIT was used to analyze digitally readable radiology reports in an untrained cohort from DOES (n = 327) to calculate sensitivity and specificity.Results: XRAIT identified a 5-fold higher number of potential significant fractures (349/5089) compared to manual case finding (70/224). 339/349 were confirmed fractures (97.1%). Only 29% of those eligible were started or recommended anti-resorptive therapy, including those seen by the fracture liaison service. XRAIT unadjusted for the local radiology reporting styles in DOES had a sensitivity of 69.6% and specificity of 95%. Conclusion: XRAIT identifies clinically significant fractures efficiently with minimal additional human resources. Its high specificity in an untrained cohort suggests it could be used at other sites. Automated methods of patient identification may assist fracture liaison services to identify fractures that still remain largely untreated.

/pdf/or29-02-natural-language-processing-of-radiology-reports-11vht7lwnm.pdf

OR29-02 Natural Language Processing of Radiology Reports Improves Identification of Patients with Fracture

Abstract Laboratory automation in clinical laboratories has made enormous differences in patient outcomes, with a wide range of tests now available that are accurate and have a rapid turnaround. Total laboratory automation (TLA) has mechanised tube handling, sample preparation and storage in general chemistry, immunoassay, haematology, and microbiology and removed most of the tedious tasks involved in those processes. However, there are still many tasks that must be performed by humans who monitor the automation lines. We are seeing an increase in the complexity of the automated laboratory through further platform consolidation and expansion of the reach of molecular genetics into the core laboratory space. This will likely require rapid implementation of enhanced real time quality control measures and these solutions will generate a significantly greater number of failure flags. To capitalise on the benefits that an improved quality control process can deliver, it will be important to ensure that an automation process is implemented simultaneously with enhanced, real time quality control measures and auto-verification of patient samples in middleware. Therefore, it appears that the best solution may be to automate those critical decisions that still require human intervention and therefore include quality control as an integral part of total laboratory automation.

The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance

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Natural language processing of radiology reports for the identification of patients with fracture

OR29-02 Natural Language Processing of Radiology Reports Improves Identification of Patients with Fracture

The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance