Nalaie, Keivan

Abstract Objectives Effective communication is crucial in healthcare, and for patients with a non-English language preference (NELP), professional interpreters are recognized as the gold standard in supporting bidirectional communication. However, interpreters are not always readily available, prompting the exploration of other options for translation and interpretation. The recent developments in artificial intelligence-based neural network translation tools, namely neural machine translation (NMT) may enable robust interpretation and translation. Materials and Methods We conducted a systematic review (SR) to evaluate the literature on NMT for this purpose. We did a comprehensive search of several databases with guidance from a professional librarian. The search was limited to the year 2000 onwards and English language. Title and abstract screening and full-text review were independently conducted by two reviewers with conflicts resolved by a third reviewer Results 2867 studies were identified with 10 studies included in the final analysis. Among these, six evaluated interpretation in real or simulated clinical settings and four examined translation of discharge materials. Google Translate and ChatGPT were assessed in several studies. Accuracy differed by language, with low-resource languages performing worse. Discussion NMT technologies in healthcare have several advantages including broad language accessibility and potential cost savings for institutions. Despite improved accuracy of these novel tools, due to possible critical errors NMT tools are not yet ready for widespread clinical use. Conclusion Future studies should focus on optimizing evaluation methods as well as how best to integrate these technologies into real-time clinical settings.

Abstract Rationale: Effective communication is crucial in healthcare, especially in acute care settings, so that patients/family members can understand treatment options, and share preferences and goals of care. For those with a non-English language preference (NELP), or who have difficulty communicating in English, professional interpreters are recognized as the gold standard in supporting bidirectional communication. However, interpreters are not always readily available for all languages or clinical settings prompting the exploration of other options for translation and interpretation. The recent developments in artificial intelligence (AI), specifically machine translation have enabled more accurate and emotional machine interpretation and translation. Methods: We conducted a systematic review to explore the usability and usefulness of AI-assisted interpretation and translation tools for improving communication between patients and clinicians including verbal and written communication. A comprehensive search of several databases including Ovid MEDLINE(R), Ovid Embase, APA PsycInfo, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus via Elsevier was performed, resulting in 2629 studies. We included studies evaluating tools for direct patient-clinician communication. For verbal communication, we included articles assessing speech interpretation, and for written communication, we focused on discharge instruction translation. Results: A total of 18 studies were included in the final analysis. Among these, 5 assessed the use of speech interpretation and 13 examined text translation of discharge materials. 14 (78%) studies found these novel AI tools beneficial for patient care, whereas 11 (62%) studies highlighted the risks, indicating that these tools can lead to serious mistakes with potential clinical harm. 5 (28%) studies reported participant satisfaction with the use of these tools in their care. Translation and interpretation accuracy varied notably across languages, with lower accuracy scores observed in languages with limited digital resources, such as Haitian Creole and Vietnamese. Conclusion: AI-assisted interpretation and translation in healthcare has several advantages including broader language accessibility than usual resources and potential cost savings for institutions and demonstrates evidence of patient satisfaction. However, these novel tools operate using multiple phases and each stage is prone to errors which may negatively impact patient care and outcomes.. AI-based interpretation and translation show promising potential, however, it is not yet ready for widespread clinical use, so professional language interpreters should be used whenever possible. Future studies should focus on evaluating accuracy as well as how best to integrate these technologies in real-time clinical settings without compromising patient safety and quality of care.

The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments.

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.