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Background/Aim: To study the impact of computer-aided detection (CADe) system on the detection rate of polyps and adenomas in colonoscopy. Materials and Methods: A total of 1026 patients were prospectively randomly scheduled for colonoscopy with (the CADe group, CADe) or without (the control group, CON) the aid of the CADe system, together with visual notification and voice alarm, so as to compare the detection rate of polyp. Results: Compared with group CON, the detection rate of adenomas increased in group CADe, the average number of adenomas increased, the number of small adenomas increased, the number of proliferative polyps increased, and the differences were statistically significant (P < 0.001), but the comparison for the number of larger adenomas showed no significant difference between the groups (P> 0.05). Conclusions: The CADe system is feasible for increasing the detection of polyps and adenomas in colonoscopy.

Artificial intelligence (AI) chatbots have been customized to deliver on-demand support for people with mental health problems. However, the effectiveness of AI chatbots in tackling mental health problems among the general public in Hong Kong remains unclear. This study aimed to develop a local AI chatbot and compare the effectiveness of the AI chatbot with a conventional nurse hotline in reducing the level of anxiety and depression among individuals in Hong Kong. This study was a pilot randomized controlled trial conducted from October 2022 to March 2023, involving 124 participants allocated randomly (1:1 ratio) into the AI chatbot and nurse hotline groups. Among these, 62 participants in the AI chatbot group and 41 in the nurse hotline group completed both the pre- and postquestionnaires, including the GAD-7 (Generalized Anxiety Disorder Scale-7), PHQ-9 (Patient Health Questionnaire-9), and satisfaction questionnaire. Comparisons were conducted using independent and paired sample t tests (2-tailed) and the χ2 test to analyze changes in anxiety and depression levels. Compared to the mean baseline score of 5.13 (SD 4.623), the mean postdepression score in the chatbot group was 3.68 (SD 4.397), which was significantly lower (P=.008). Similarly, a reduced anxiety score was also observed after the chatbot test (pre vs post: mean 4.74, SD 4.742 vs mean 3.4, SD 3.748; P=.005), respectively. No significant differences were found in the pre-post scores for either depression (P=.38) or anxiety (P=.19). No statistically significant difference was observed in service satisfaction between the two platforms (P=.32). The AI chatbot was comparable to the traditional nurse hotline in alleviating participants' anxiety and depression after responding to inquiries. Moreover, the AI chatbot has shown potential in alleviating short-term anxiety and depression compared to the nurse hotline. While the AI chatbot presents a promising solution for offering accessible strategies to the public, more extensive randomized controlled studies are necessary to further validate its effectiveness.

Exercise therapy is strongly recommended as a treatment for chronic nonspecific low back pain (CNSLBP). However, therapist-guided exercise therapy requires significant medical resources. Ordinary digital telerehabilitation affects efficacy due to a lack of guidance and dynamic support. Artificial intelligence (AI)-assisted interactive health promotion systems may solve these problems. We aimed to explore whether AI-assisted multimodal exercise telerehabilitation is superior to conventional telerehabilitation in the treatment of people with CNSLBP. This study was a prospective, double-arm, open-label, randomized clinical controlled trial. People with CNSLBP were randomly allocated to either the AI or video group, receiving AI-assisted multimodal exercise therapy or conventional video guidance, respectively, via a WeChat application add-in. The multimodal exercise consisted of deep core muscle, flexibility, Mackenzie, and breathing exercises. The exercises were performed for 30-45 minutes per session, 3 times a week, for 4 weeks. Participants underwent face-to-face assessment at baseline and week 4, and web-based assessment at weeks 2 and 8. The primary outcome was the change in Numerical Rating Scale (NRS) relative to baseline at week 4. Secondary outcomes included changes in the Roland-Morris Disability Questionnaire (RMDQ), Oswestry Disability Index (ODI), Pain Castastrophizing Scale (PCS), Timed Up-and-Go (TUG) test, and thickness of the transverse abdominus (TrA) and multifidus (MF) muscles relative to baseline at week 4. Generalized estimating equation and covariance were used to examine the efficacy of the interventions. A total of 38 participants (19 participants per group) were recruited. Eighteen participants in the AI group and 16 participants in the video group completed and were included in the final analysis. There was a significant difference in NRS at week 4 between the AI group and video group (most severe NRS: -3.00 vs -1.50; adjusted mean difference -1.08, 95% CI -1.68 to -0.49; P<.001; mean NRS: -2.61 vs -1.62; adjusted mean difference -0.67, 95% CI -1.19 to -0.15; P=.01). The difference in most severe NRS persisted until week 8 (-3.06 vs -1.69; adjusted mean difference -0.95, 95% CI -1.73 to -0.18; P=.02). Compared with the video group at week 4, the AI group showed significant improvement in secondary outcomes, including RMDQ, PCS, and core muscle thickness of left TrA, right TrA, left MF, and right MF. We showed that 4 weeks of telerehabilitation based on AI-assisted multimodal exercise has better therapeutic effects compared to conventional exercise telerehabilitation in people with CNSLBP. This study provides guidance for developing effective real-time home-based exercise therapies for people with CNSLBP, which may help reduce economic and human resource costs associated with treatment.

Challenges at the interface of medical statistics and AI are population inference vs. prediction, generalizability, reproducibility and interpretation of evidence, and stability and statistical guarantees.

BackgroundWe have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural network for assisting endoscopists in detecting colorectal lesions during colonoscopy. The aim of this study was to clarify whether adenoma miss rate (AMR) could be reduced with CADe assistance during screening and surveillance colonoscopy.MethodsThis study was a multicenter randomized controlled trial. Patients aged 40 to 80 years who were referred for colorectal screening or surveillance at four sites in Japan were randomly assigned at a 1:1 ratio to either the “standard colonoscopy (SC)-first group” or the “CADe-first group” to undergo a back-to-back tandem procedure. Tandem colonoscopies were performed on the same day for each participant by the same endoscopist in a preassigned order. All polyps detected in each pass were histopathologically diagnosed after biopsy or resection.ResultsA total of 358 patients were enrolled and 179 patients were assigned to the SC-first group or CADe-first group. The AMR of the CADe-first group was significantly lower than that of the SC-first group (13.8% vs. 36.7%, P < 0.0001). Similar results were observed for the polyp miss rate (14.2% vs. 40.6%, P < 0.0001) and sessile serrated lesion miss rate (13.0% vs. 38.5%, P = 0.03). The adenoma detection rate of CADe-assisted colonoscopy was 64.5%, which was significantly higher than that of standard colonoscopy (53.6%; P = 0.036).ConclusionOur study results first showed a reduction in the AMR when assisting with CADe based on deep learning in a multicenter randomized controlled trial.

The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learning. The increased availability of these technologies offers opportunities for improving important aspects of rheumatology, including access, outcomes, adherence and research. However, despite its growth in some areas, particularly with non-health-care consumers, digital health technology has not substantially changed the delivery of rheumatology care. This Review discusses key barriers and opportunities to improve application of digital health technologies in rheumatology. Key topics include smart design, voice enablement and the integration of electronic patient-reported outcomes. Smart design involves active engagement with the end users of the technologies, including patients and clinicians through focus groups, user testing sessions and prototype review. Voice enablement using voice assistants could be critical for enabling patients with hand arthritis to effectively use smartphone apps and might facilitate patient engagement with many technologies. Tracking many rheumatic diseases requires frequent monitoring of patient-reported outcomes. Current practice only collects this information sporadically, and rarely between visits. Digital health technology could enable patient-reported outcomes to inform appropriate timing of face-to-face visits and enable improved application of treat-to-target strategies. However, best practice standards for digital health technologies do not yet exist. To achieve the potential of digital health technology in rheumatology, rheumatology professionals will need to be more engaged upstream in the technology design process and provide leadership to effectively incorporate the new tools into clinical care.Digital health technologies (DHTs) have a variety of interesting current and possible future applications in rheumatology. In this article, the authors describe some of the key barriers that prevent DHT integration into rheumatology care and discuss ways in which these barriers could be addressed.

Background Integrating artificial intelligence (AI) into intensive care practices can enhance patient care by providing real-time predictions and aiding clinical decisions. However, biases in AI models can undermine diversity, equity, and inclusion (DEI) efforts, particularly in visual representations of healthcare professionals. This work aims to examine the demographic representation of two AI text-to-image models, Midjourney and ChatGPT DALL-E 2, and assess their accuracy in depicting the demographic characteristics of intensivists. Methods This cross-sectional study, conducted from May to July 2024, used demographic data from the USA workforce report (2022) and intensive care trainees (2021) to compare real-world intensivist demographics with images generated by two AI models, Midjourney v6.0 and ChatGPT 4.0 DALL-E 2. A total of 1,400 images were generated across ICU subspecialties, with outcomes being the comparison of sex, race/ethnicity, and age representation in AI-generated images to the actual workforce demographics. Results The AI models demonstrated noticeable biases when compared to the actual U.S. intensive care workforce data, notably overrepresenting White and young doctors. ChatGPT-DALL-E2 produced less female (17.3% vs 32.2%, p  < 0.0001), more White (61% vs 55.1%, p  = 0.002) and younger (53.3% vs 23.9%, p  < 0.001) individuals. While Midjourney depicted more female (47.6% vs 32.2%, p  < 0.001), more White (60.9% vs 55.1%, p  = 0.003) and younger intensivist (49.3% vs 23.9%, p  < 0.001). Substantial differences between the specialties within both models were observed. Finally when compared together, both models showed significant differences in the Portrayal of intensivists. Conclusions Significant biases in AI images of intensivists generated by ChatGPT DALL-E 2 and Midjourney reflect broader cultural issues, potentially perpetuating stereotypes of healthcare worker within the society. This study highlights the need for an approach that ensures fairness, accountability, transparency, and ethics in AI applications for healthcare.

Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Early detection and intervention can prevent blindness; however, many patients do not receive their recommended annual diabetic eye examinations, primarily owing to limited access. To evaluate the safety and accuracy of an artificial intelligence (AI) system (the EyeArt Automated DR Detection System, version 2.1.0) in detecting both more-than-mild diabetic retinopathy (mtmDR) and vision-threatening diabetic retinopathy (vtDR). A prospective multicenter cross-sectional diagnostic study was preregistered (NCT03112005) and conducted from April 17, 2017, to May 30, 2018. A total of 942 individuals aged 18 years or older who had diabetes gave consent to participate at 15 primary care and eye care facilities. Data analysis was performed from February 14 to July 10, 2019. Retinal imaging for the autonomous AI system and Early Treatment Diabetic Retinopathy Study (ETDRS) reference standard determination. Primary outcome measures included the sensitivity and specificity of the AI system in identifying participants' eyes with mtmDR and/or vtDR by 2-field undilated fundus photography vs a rigorous clinical reference standard comprising reading center grading of 4 wide-field dilated images using the ETDRS severity scale. Secondary outcome measures included the evaluation of imageability, dilated-if-needed analysis, enrichment correction analysis, worst-case imputation, and safety outcomes. Of 942 consenting individuals, 893 patients (1786 eyes) met the inclusion criteria and completed the study protocol. The population included 449 men (50.3%). Mean (SD) participant age was 53.9 (15.2) years (median, 56; range, 18-88 years), 655 were White (73.3%), and 206 had type 1 diabetes (23.1%). Sensitivity and specificity of the AI system were high in detecting mtmDR (sensitivity: 95.5%; 95% CI, 92.4%-98.5% and specificity: 85.0%; 95% CI, 82.6%-87.4%) and vtDR (sensitivity: 95.1%; 95% CI, 90.1%-100% and specificity: 89.0%; 95% CI, 87.0%-91.1%) without dilation. Imageability was high without dilation, with the AI system able to grade 87.4% (95% CI, 85.2%-89.6%) of the eyes with reading center grades. When eyes with ungradable results were dilated per the protocol, the imageability improved to 97.4% (95% CI, 96.4%-98.5%), with the sensitivity and specificity being similar. After correcting for enrichment, the mtmDR specificity increased to 87.8% (95% CI, 86.3%-89.5%) and the sensitivity remained similar; for vtDR, both sensitivity (97.0%; 95% CI, 91.2%-100%) and specificity (90.1%; 95% CI, 89.4%-91.5%) improved. This prospective multicenter cross-sectional diagnostic study noted safety and accuracy with use of the EyeArt Automated DR Detection System in detecting both mtmDR and, for the first time, vtDR, without physician assistance. These findings suggest that improved access to accurate, reliable diabetic eye examinations may increase adherence to recommended annual screenings and allow for accelerated referral of patients identified as having vtDR.

BackgroundSearch engines often serve as a primary resource for patients to obtain drug information. However, the search engine market is rapidly changing due to the introduction of artificial intelligence (AI)-powered chatbots. The consequences for medication safety when patients interact with chatbots remain largely unexplored.ObjectiveTo explore the quality and potential safety concerns of answers provided by an AI-powered chatbot integrated within a search engine.MethodologyBing copilot was queried on 10 frequently asked patient questions regarding the 50 most prescribed drugs in the US outpatient market. Patient questions covered drug indications, mechanisms of action, instructions for use, adverse drug reactions and contraindications. Readability of chatbot answers was assessed using the Flesch Reading Ease Score. Completeness and accuracy were evaluated based on corresponding patient drug information in the pharmaceutical encyclopaedia drugs.com. On a preselected subset of inaccurate chatbot answers, healthcare professionals evaluated likelihood and extent of possible harm if patients follow the chatbot’s given recommendations.ResultsOf 500 generated chatbot answers, overall readability implied that responses were difficult to read according to the Flesch Reading Ease Score. Overall median completeness and accuracy of chatbot answers were 100.0% (IQR 50.0–100.0%) and 100.0% (IQR 88.1–100.0%), respectively. Of the subset of 20 chatbot answers, experts found 66% (95% CI 50% to 85%) to be potentially harmful. 42% (95% CI 25% to 60%) of these 20 chatbot answers were found to potentially cause moderate to mild harm, and 22% (95% CI 10% to 40%) to cause severe harm or even death if patients follow the chatbot’s advice.ConclusionsAI-powered chatbots are capable of providing overall complete and accurate patient drug information. Yet, experts deemed a considerable number of answers incorrect or potentially harmful. Furthermore, complexity of chatbot answers may limit patient understanding. Hence, healthcare professionals should be cautious in recommending AI-powered search engines until more precise and reliable alternatives are available.

To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison. The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders.