Introduction: There is no recent consensus on the effectiveness of teledentistry versus in-person examination in the diagnosis of dental caries, especially after the COVID-19 pandemic. Objective: To assess the diagnostic accuracy of teledentistry versus in-person examination for dental caries diagnosis (PROSPERO #CRD42023410962). Methods: This systematic review and meta-analysis compared the effectiveness of teledentistry versus in-person examination for dental caries diagnosis. The eligibility criteria were peer-reviewed studies published in English between January 2013 and December 2021 that reported diagnostic parameters (specificity and sensitivity) for caries detection in primary and permanent dentition. Articles were extracted using search strategies from PubMed and CINAHL databases and screened using PRISMA-DTA guidelines, following a review for quality assessment and risk of bias using the QUADAS-2 and JBI Critical Appraisal Checklists. Meta-analysis was conducted in R using the MADA package. A descriptive analysis of the sensitivity, specificity, diagnostic odds ratio, and confidence intervals was performed with respective forest plots. Heterogeneity was assessed using Cochrane and Higgins's 2 tests. Univariate measures of diagnostic accuracy were performed based on the DerSimonian-Laird random effect and reported summary diagnostic odds ratios. Results: Twelve studies met the inclusion criteria and were reviewed and included in the meta-analysis. The diagnostic parameters ranged from 45.6% to 88.3% for sensitivity, 55.2% to 98.3% for specificity, 79% to 92% for positive predictive value, 48% to 97% for negative predictive value, and 70% to 96% for accuracy. The κ scores ranged from 0.46 to 0.89 for teledentistry modalities. Tests for equality of sensitivities and specificities were significant ( < 0.001). The studies were not heterogeneous with Cochran's : 14.502 ( = 0.206) and Higgins's 2 of 24%. The multivariable analysis showed a diagnostic odds ratio based on the DerSimonian-Laird random effect of 35.14, which indicates that the odds of caries detection via teledentistry is 35 times more true positive (i.e., correctly identifying a positive condition) than false positive. Conclusions: Diagnosis of caries via teledentistry is effective and comparable to in-person diagnosis. Remote assessments are consistent in diagnostic accuracy for caries.Knowledge Transfer Statement:This systematic review and meta-analysis added to the evidence about using teledentistry assessment as a diagnostically accurate tool to detect dental caries. Using teledentistry dental practices could promote greater access to dental and oral health care in the absence of in-person assessment.

The Psychiatric Pediatric Issues Task Force of the International League Against Epilepsy (ILAE) aimed to develop recommendations for the diagnosis and treatment of anxiety and depression in children and adolescents with epilepsy. The Task Force conducted a systematic review and identified two studies that assessed the accuracy of four screening measures for depression and anxiety symptoms compared with a psychiatric interview. Nine studies met the eligibility criteria for treatment of anxiety and depressive disorders or symptoms. The risk of bias and certainty of evidence were assessed. The evidence generated by this review followed by consensus where evidence was missing generated 47 recommendations. Those with a high level of agreement (≥80%) are summarized. Diagnosis: (1) Universal screening for anxiety and depression is recommended. Closer surveillance is recommended for children after 12 years, at higher risk (e.g., suicide-related behavior), with subthreshold symptoms, and experiencing seizure worsening or therapeutic modifications. (2) Multiple sources of ascertainment and a formal screening are recommended. Clinical interviews are recommended whenever possible. The healthcare provider must always explain that symptom recognition is essential to optimize treatment outcomes and reduce morbidity. (3) Questioning about the relationship between symptoms of anxiety or depression with seizure worsening/control and behavioral adverse effects of antiseizure medications is recommended. Treatment: (1) An individualized treatment plan is recommended. (2) For mild depression, active monitoring must be considered. (3) Referral to a mental health care provider must be considered for moderate to severe depression and anxiety. (4) Clinical care pathways must be developed. (5) Psychosocial interventions must be tailored and age-appropriate. (6) Healthcare providers must monitor children with epilepsy who are prescribed antidepressants, considering symptoms and functioning that may not improve simultaneously. (7) Caregiver education is essential to ensure treatment adherence. (8) A shared-care model involving all healthcare providers is recommended for children and adolescents with epilepsy and mental health disorders. We identified clinical decisions in the management of depression and anxiety that lack solid evidence and provide consensus-based guidance to address the care of children and adolescents with epilepsy.

Ethnopharmacological relevance: Tanreqing injection (TRQI) is an intravenous herbal preparation derived from 5 types of traditional Chinese medicines including Scutellariae Radix, Lonicerae Japonicae Flos, Forsythiae Fructus, bear bile powder and goral horn, incorporating baicalin, chlorogenic acid, ursodeoxycholic acid, and goose deoxycholic acid and other compounds known for anti-inflammatory properties, is widely used in China to treat cough caused by acute trachea-bronchitis disease (ATB). Aim of the study: To investigate the clinical efficacy and safety of Tanreqing injection (TRQI) with and without Western medicine (WM) for cough caused by acute trachea-bronchitis (ATB). Materials and methods: We systematically searched eight databases, including CENTRAL, Embase, PubMed, Science Direct, Wiley, China National Knowledge Infrastructure, Chinese Biomedical Literature Database and WanFang, from inception to August 2023 for randomized clinical trials (RCTs) on TRQI for cough caused by ATB. The critical outcomes of interest were time to symptom disappearance, including time for cough symptom to disappear and time to improve cough and sputum production. Important outcomes included symptom disappearance rate, adverse events (AEs) and lung function. We carried out random-effects meta-analysis using Review Manager 5.4 and assessed the certainty of evidence utilizing the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: A total of 2872 citations were identified by our search, of which 26 eligible RCTs enrolled 2731 participants. Low to moderate certainty evidence showed that when compared with WM, TRQI plus WM treatment was associated with a favorable effect on the time for cough symptom to disappear (MD-2.21 d, 95% CI-2.64 to-1.78), time to improve cough and sputum production (MD-0.68 d, 95% CI-0.83 to-0.53), symptom disappearance rate (RR 1.37, 95% CI 1.20 to 1.55), forced vital capacity, and forced expiratory volume in 1 s (MD 0.38 L, 95% CI 0.26 to 0.50; MD 2.92%, 95% CI 1.29 to 4.56, respectively). In terms of AEs, there was no association between TRQI plus WM and WM (RR 0.55, 95% CI 0.14 to 2.21; low-certainty evidence). Very low certainty evidence showed that TRQI alone was associated with reduced time to improve cough and sputum (MD-0.14 d, 95% CI-0.26 to-0.02) and increased symptom disappearance rate (RR 1.89, 95% CI 1.24 to 2.88; low certainty evidence) compared to WM.

Poor cancer survival outcomes in sub-Saharan Africa (SSA) have been linked to delays in diagnosis and treatment. Here we present a detailed overview of the qualitative literature evaluating the barriers to receiving timely diagnosis and treatment of cancer in SSA. The PubMed, EMBASE, CINAHL, PsycINFO databases were searched to identify qualitative studies reporting on barriers to timely diagnosis of cancer in SSA published between 1995 and 2020. A systematic review methodology was applied, including quality assessment and narrative data synthesis. We identified 39 studies, of which 24 focused on breast or cervical cancer. Only one study focused on prostate cancer and one on lung cancer. When exploring factors contributing to delays, six key themes emerged from the data. The first theme was health service barriers, which included: (i) inadequate numbers of trained specialists; (ii) limited knowledge of cancer among healthcare providers; (iii) poor co-ordination of care; (iv) inadequately resourced health facilities; (v) negative attitudes of healthcare providers towards patients; (vi) high cost of diagnostic and treatment services. The second key theme was patient preference for complementary and alternative medicine; the third was the limited understanding of cancer among the population. The fourth barrier was a patient's personal and family obligations; the fifth was the perceived impact of cancer and its treatment on sexuality, body image and relationships. Finally, the sixth was the stigma and discrimination faced by patients following a diagnosis of cancer. In conclusion, health system, patient level and societal factors all influence the likelihood of timely diagnosis and treatment for cancer in SSA. The results provide a focus for targeting health system interventions, particular with regards to awareness and understanding of cancer in the region.

BACKGROUND: In the context of a deepening global shortage of health workers and, in particular, the COVID-19 pandemic, there is growing international interest in, and use of, online symptom checkers (OSCs). However, the evidence surrounding the triage and diagnostic accuracy of these tools remains inconclusive. OBJECTIVE: This systematic review aimed to summarize the existing peer-reviewed literature evaluating the triage accuracy (directing users to appropriate services based on their presenting symptoms) and diagnostic accuracy of OSCs aimed at lay users for general health concerns. METHODS: Searches were conducted in MEDLINE, Embase, CINAHL, Health Management Information Consortium (HMIC), and Web of Science, as well as the citations of the studies selected for full-text screening. We included peer-reviewed studies published in English between January 1, 2010, and February 16, 2022, with a controlled and quantitative assessment of either or both triage and diagnostic accuracy of OSCs directed at lay users. We excluded tools supporting health care professionals, as well as disease- or specialty-specific OSCs. Screening and data extraction were carried out independently by 2 reviewers for each study. We performed a descriptive narrative synthesis. RESULTS: A total of 21,296 studies were identified, of which 14 (0.07%) were included. The included studies used clinical vignettes, medical records, or direct input by patients. Of the 14 studies, 6 (43%) reported on triage and diagnostic accuracy, 7 (50%) focused on triage accuracy, and 1 (7%) focused on diagnostic accuracy. These outcomes were assessed based on the diagnostic and triage recommendations attached to the vignette in the case of vignette studies or on those provided by nurses or general practitioners, including through face-to-face and telephone consultations. Both diagnostic accuracy and triage accuracy varied greatly among OSCs. Overall diagnostic accuracy was deemed to be low and was almost always lower than that of the comparator. Similarly, most of the studies (9/13, 69 %) showed suboptimal triage accuracy overall, with a few exceptions (4/13, 31%). The main variables affecting the levels of diagnostic and triage accuracy were the severity and urgency of the condition, the use of artificial intelligence algorithms, and demographic questions. However, the impact of each variable differed across tools and studies, making it difficult to draw any solid conclusions. All included studies had at least one area with unclear risk of bias according to the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool. CONCLUSIONS: Although OSCs have potential to provide accessible and accurate health advice and triage recommendations to users, more research is needed to validate their triage and diagnostic accuracy before widescale adoption in community and health care settings. Future studies should aim to use a common methodology and agreed standard for evaluation to facilitate objective benchmarking and validation.

Recent years have seen the emergence and application of artificial intelligence (AI) in diagnostic decision support systems. There are approximately 80 etiologies that can underly uveitis, some very rare, and AI may lend itself to their detection. This synthesis of the literature selected articles that focused on the use of AI in determining the diagnosis, classification, and underlying etiology of uveitis. The AI-based systems demonstrated relatively good performance, with a classification accuracy of 93-99% and a sensitivity of at least 80% for identifying the two most probable etiologies underlying uveitis. However, there were limitations to the evidence. Firstly, most data were collected retrospectively with missing data. Secondly, ophthalmic, demographic, clinical, and ancillary tests were not reliably integrated into the algorithms' dataset. Thirdly, patient numbers were small, which is problematic when aiming to discriminate rare and complex diagnoses. In conclusion, the data indicate that AI has potential as a diagnostic decision support system, but clinical applicability is not yet established. Future studies and technologies need to incorporate more comprehensive clinical data and larger patient populations. In time, these should improve AI-based diagnostic tools and help clinicians diagnose, classify, and manage patients with uveitis.

BACKGROUND: The usage of artificial intelligence in medical image analysis has significantly surpassed that of earlier related technologies. This paper aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) based-deep learning models for breast cancer detection. METHOD: We used the PICO (Patient/Population/Problem, Intervention, Comparison, Outcome) scheme to formulate the research question and construct our search terms. Studies were systematically examined from the available literature using the constructed search terms from PubMed, and ScienceDirect according to the PRISMA guidelines. The quality of the included studies was assessed using the QUADAS-2 checklist. The characteristics of each included study such as the study design, population, index test, and reference standard, were extracted. The sensitivity, specificity, and AUC for each study were also reported. RESULTS: In this systematic review, 14 studies were analyzed. Eight studies showed that AI was more accurate than radiologists in evaluating mammographic images, while one comprehensive study found AI to be less precise. Studies that reported sensitivity and specificity without radiologist intervention showed performance scores ranging from 16.0% to 89.71%. With radiologist intervention, sensitivity was between 62% to 86%. Only three studies reported a specificity of 73.5% to 79%. The AUC of the studies was between 0.79 and 0.95. Thirteen studies were retrospective, and one was prospective. CONCLUSION: There's inadequate evidence on the effectiveness of AI-based deep learning for breast cancer screening in clinical settings. More research is needed, including studies evaluating accuracy, RCTs, and large-scale cohort studies. This systematic review found that AI-based deep learning improves radiologists' accuracy, especially for novice radiologists. Younger, tech-savvy clinicians may be more accepting of AI. Although it can't replace radiologists, the encouraging results suggest it will play a significant role in identifying breast cancer in the future.

The global rise in deaths caused by pulmonary tuberculosis (TB) has placed increased pressure on overburdened healthcare systems to provide TB diagnostic services. Artificial intelligence-based computer-aided diagnosis (AI-based CAD) promises to be a powerful tool in responding to this health challenge by providing actionable outputs which support the diagnostic accuracy and efficiency of clinicians. However, these technologies must first be extensively evaluated to understand their impact and risks before pursuing wide-scale deployment. Yet, health technology assessments for them in real world settings have been limited. Comprehensive evaluation demands consideration of technical safety, human factors, and health impacts to generate robust evidence and understand what is needed for long-term sustainable benefit realisation. This work-in progress study presents a three-stage methodological approach that will be used to guide the data collection and analysis process for evaluating the impact of implementing a commercial AI-based CAD system for TB diagnosis in a real-world radiological setting.

BACKGROUND: The present study was conducted to identify barriers and facilitators of early diagnosis and treatment of gastric cancer. METHODS: Comprehensive search was conducted on 2021 in various databases, including Medline, Web of science, and Scopus. Keywords such as gastric cancer, screening programs, endoscopy, barriers, facilitators, and factor were used for the search, as single or in combination. Also a manual search was done in valid scientific journals to find related full-text articles. The search results were entered into the Endonote-X8 software, which automatically removes duplicate articles. Then, the title and the abstract and finally, the text of the articles were studied. Articles that addressed barriers and facilitators of early diagnosis and treatment of gastric cancer were included. RESULTS: In according to the results of 22 included articles, delay time in the diagnosis and treatment of gastric cancer were high, and factors such as age, sex, race and ethnicity, economic and social status, access to diagnostic services, implementation of screening programs, type and accuracy of screening methods, use of insurance services, error in care services, and presence of gastrointestinal symptoms were considered to be contributing factors in this regard. CONCLUSIONS: It seems that to reduce delay in the diagnosis and treatment of gastric cancer, factors such as implementing screening programs using acceptable methods with high sensitivity and accuracy with a high level of participation, increasing insurance coverage and reducing the share of people in payments, increasing people's access to diagnostic services, educating people about the symptoms and risks of gastric cancer, undertaking proper follow-up in patients and suspects cases identified in screening, as well as increasing patients' access to medical services through financial and insurance support are significantly important.

BACKGROUND: Telehealth can reduce the gap between developmental concern and diagnosis. Evaluation of telehealth methods is needed for providers to make decisions about using telediagnostic assessments. AIM: This systematic review examined telehealth in facilitating a diagnosis for children with developmental concerns and assessed 1) study characteristics and type of diagnostic evaluation; 2) comparison of telehealth technologies to in-person diagnostic methods; 3) feasibility and acceptability of telehealth technologies; and 4) methodological quality. METHOD AND PROCEDURES: Peer-reviewed studies from PsycINFO, CINAHL, Web of Science, PubMed, Embase, and Cochrane published January 2000-July 2021 were searched using "telehealth" AND "developmental concern" AND "diagnosis". Data extraction included study characteristics, diagnostic evaluation, technology, diagnostic accuracy, feasibility, and acceptability. Methodological quality was assessed using NHLBI tools. OUTCOMES AND RESULTS: Nine studies met inclusion. Children with suspected FAS, social-emotional concerns, suspected genetic conditions, and failed hearing screenings received a telediagnosis. Evaluations included dysmorphology, feeding, neurological, developmental, audiological, and psychiatric. Seven studies used videoconferencing in real-time and two used Store-and-Forward methods. High diagnostic agreement occurred between face-to-face and remote methods. Stakeholders reported high satisfaction and feasibility. Many of the studies were rated as fair quality. CONCLUSIONS AND IMPLICATIONS: Findings underscore partnership models between local providers and remote specialists. Rigorous study designs with larger samples covering a wider range of developmental domains are needed to provide a stronger empirical base for providers.

BACKGROUND: Innovative technologies, such as machine learning, big data, and artificial intelligence (AI) are approaches adopted for personalized medicine, and psychological interventions and diagnosis are facing huge paradigm shifts. In this literature review, we aim to highlight potential applications of AI on psychological interventions and diagnosis. METHODS: This literature review manifest studies that discuss how innovative technology as deep learning (DL) and AI is affecting psychological assessment and psychotherapy, we performed a search on PUBMED, and Web of Science using the terms "psychological interventions," "diagnosis on mental health disorders," "artificial intelligence," and "deep learning." Only studies considering patients' datasets are considered. RESULTS: Nine studies met the inclusion criteria. Beneficial effects on clinical symptoms or prediction were shown in these studies, but future study is needed to determine the long-term effects. LIMITATIONS: The major limitation for the current study is the small sample size, and lies in the lack of long-term follow-up-controlled studies for a certain symptom. CONCLUSIONS: AI such as DL applications showed promising results on clinical practice, which could lead to profound impact on personalized medicine for mental health conditions. Future studies can improve furthermore by increasing sample sizes and focusing on ethical approvals and adherence for online-therapy.

The high mortality rate in Tuberculosis (TB) burden regions has increased significantly in the last decades. Despite the possibility of treatment for TB, high burden regions still suffer inadequate screening tools, which result in diagnostic delay and misdiagnosis. These challenges have led to the development of Computer-Aided Diagnostic (CAD) system to detect TB automatically. There are several ways of screening for TB, but Chest X-Ray (CXR) is more prominent and recommended due to its high sensitivity in detecting lung abnormalities. This paper presents the results of a systematic review based on PRISMA procedures that investigate state-of-the-art Deep Learning techniques for screening pulmonary abnormalities related to TB. The systematic review was conducted using an extensive selection of scientific databases as reference sources that grant access to distinctive articles in the field. Four scientific databases were searched to retrieve related articles. Inclusion and exclusion criteria were defined and applied to each article to determine those included in the study. Out of the 489 articles retrieved, 62 were included. Based on the findings in this review, we conclude that CAD systems are promising in tackling the challenges of the TB epidemic and made recommendations for improvement in future studies.

OBJECTIVE: To explore digital health interventions that have been used to support pregnant women at high risk for pre-eclampsia/eclampsia (HRPE/E) in low-income and middle-income countries (LMICs). DESIGN: Scoping review. DATA SOURCE: EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and CINAHL were searched between 1 January 2000 and 20 October 2020. ELIGIBILITY CRITERIA: The review included original research studies that were published in English, involved pregnant women at HRPE/E and implemented digital health interventions for PE/E in LMICs. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently completed the data extraction for each of the 19 final articles. An inductive approach was used to thematically organise and summarise the results from the included articles. RESULTS: A total of 19 publications describing 7 unique studies and 9 different digital health interventions were included. Most studies were conducted in South Asia and sub-Saharan Africa (n=16). Of nine unique digital health interventions, two served the purpose of predicting risk for adverse maternal health outcomes while seven focused on monitoring high-risk pregnant women for PE/E. Both of these purposes used mobile phone applications as interface to facilitate data collection, decision making, and communication between health workers and pregnant women. The review identified key functions of interventions including data collection, prediction of adverse maternal outcomes, integrated diagnostic and clinical decision support, and personal health tracking. The review reported three major outcomes: maternal health outcomes including maternal and neonatal morbidity and mortality (n=4); usability and acceptability including ease-of-use, and perceived usefulness, (n=5); and intervention feasibility and fidelity including accuracy of device, and intervention implementation (n=7). CONCLUSION: Although the current evidence base shows some potential for the use of digital health interventions for PE/E, more prospective experimental and longitudinal studies are needed prior to recommending the use of digital health interventions for PE/E.

Numerous factors contribute to the low adherence to lung cancer screening (LCS) programs. A theory-informed approach to identifying the obstacles and facilitators to LCS uptake is required. This study aimed to identify, assess, and synthesize the available literature at the individual and healthcare provider (HCP) levels based on a social-ecological model and identify gaps to improve practice and policy decision-making. Systematic searches were conducted in nine electronic databases from inception to December 31, 2020. We also searched Google Scholar and manually examined the reference lists of systematic reviews to include relevant articles. Primary studies were scored for quality assessment. Among 3938 potentially relevant articles, 36 studies, including 25 quantitative and 11 qualitative studies, were identified for inclusion in the review. Fifteen common factors were extracted from 34 studies, including nine barriers and six facilitators. The barriers included individual factors (n = 5), health system factors (n = 3), and social/environmental factors (n = 1). The facilitators included only individual factors (n = 6). However, two factors, age and screening harm, remain mixed. This systematic review identified and combined barriers and facilitators to LCS uptake at the individual and HCP levels. The interaction mechanisms among these factors should be further explored, which will allow the construction of tailored LCS recommendations or interventions for the Chinese context.

OBJECTIVES: Recent innovations in prostate cancer diagnosis include new biomarkers and more accurate biopsy methods. This study assesses the evidence base on cost-effectiveness of these developments (eg, Prostate Health Index and magnetic resonance imaging [MRI]-guided biopsy) and identifies areas of improvement for future cost-effectiveness models. METHODS: A systematic review using the National Health Service Economic Evaluation Database, MEDLINE, Embase, Health Technology Assessment databases, National Institute for Health and Care Excellence guidelines, and United Kingdom National Screening Committee guidance was performed, between 2009 and 2021. Relevant data were extracted on study type, model inputs, modeling methods and cost-effectiveness conclusions, and results narratively synthesized. RESULTS: A total of 22 model-based economic evaluations were included. A total of 11 compared the cost-effectiveness of new biomarkers to prostate-specific antigen testing alone and all found biomarkers to be cost saving. A total of 8 compared MRI-guided biopsy methods to transrectal ultrasound-guided methods and found MRI-guided methods to be most cost-effective. Newer detection methods showed a reduction in unnecessary biopsies and overtreatment. The most cost-effective follow-up strategy in men with a negative initial biopsy was uncertain. Many studies did not model for stage or grade of cancer, cancer progression, or the entire testing and treatment pathway. Few fully accounted for uncertainty. CONCLUSIONS: This review brings together the cost-effectiveness literature for novel diagnostic methods in prostate cancer, showing that most studies have found new methods to be more cost-effective than standard of care. Several limitations of the models were identified, however, limiting the reliability of the results. Areas for further development include accurately modeling the impact of early diagnostic tests on long-term outcomes of prostate cancer and fully accounting for uncertainty.

Objective: to evaluate the cost-effectiveness of point-of-care leukocyte analyzers to aid in the diagnosis of airway infections in the SUS.Method: this is a cost-effectiveness study using a deterministic model developed from a decision tree.Results: the use of devices to perform diagnostic tests at the point of treatment has become increasingly popular and accepted around the world in view of the increased demand for care and the need to reduce the time it takes to return exam results to optimization of outcomes. After the Roll Back of the decision tree, the most cost-effective strategy was a white blood cell count-driven clinical investigation using the point-of-care analyzer to guide antibiotic prescribing.Conclusion: Evidence suggests that the use of POC analyzers for WBC counts and differentials at the point of care is a cost-effective alternative as part of a strategy to aid in the diagnosis and therapeutic management of cases of non-specific acute respiratory infections.

Background: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine

BACKGROUND: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. OBJECTIVE: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. METHODS: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. RESULTS: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. CONCLUSIONS: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

PURPOSE: Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices. PATIENTS AND METHODS: We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words. RESULTS: Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented. CONCLUSION: Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.

BACKGROUND: Point of care testing (POCT) for infectious diseases is testing conducted near the patient. It allows clinicians to offer the most appropriate treatment more quickly. As POCT devices have increased in accuracy and become more cost-effective, their use has grown, but a systematic assessment of their use for clinical and public health management of infectious diseases in EU/EEA countries has not been previously undertaken. METHODS: A scoping review of the literature on POCT in EU/ EEA countries as at November 2019, and a survey of key stakeholders. RESULTS: 350 relevant articles were identified and 54 survey responses from 26 EU/EEA countries were analysed. POCT is available for a range of infectious diseases and in all countries responding to the survey (for at least one disease). POCT is commonly available for influenza, HIV/AIDS, Legionnaires' disease and malaria, where it is used in at least half of EU/EEA countries. While POCT has the potential to support many improvements to clinical care of infectious diseases (e.g., faster diagnosis, more appropriate use of antimicrobials), the results suggest POCT is infrequently used to support public health functions (e.g., disease surveillance and reporting). CONCLUSION: Although POCT is in use to some extent in all EU/EEA countries, the full benefits of POCT in wider public health functions have yet to be realised. Further research on barriers and facilitators to implementation is warranted.