BACKGROUND: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes.
OBJECTIVE: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA.
METHODS: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation).
RESULTS: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence.
CONCLUSIONS: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI\'s impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

OBJECTIVE: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.
METHODS: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher\'s exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.
RESULTS: A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).
CONCLUSIONS: Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

BACKGROUND: Laparoscopic pancreatoduodenectomy (LPD) is one of the most challenging operations and has a long learning curve. Artificial intelligence (AI) automated surgical phase recognition in intraoperative videos has many potential applications in surgical education, helping shorten the learning curve, but no study has made this breakthrough in LPD. Herein, we aimed to build AI models to recognize the surgical phase in LPD and explore the performance characteristics of AI models.
METHODS: Among 69 LPD videos from a single surgical team, we used 42 in the building group to establish the models and used the remaining 27 videos in the analysis group to assess the models\' performance characteristics. We annotated 13 surgical phases of LPD, including 4 key phases and 9 necessary phases. Two minimal invasive pancreatic surgeons annotated all the videos. We built two AI models for the key phase and necessary phase recognition, based on convolutional neural networks. The overall performance of the AI models was determined mainly by mean average precision (mAP).
RESULTS: Overall mAPs of the AI models in the test set of the building group were 89.7% and 84.7% for key phases and necessary phases, respectively. In the 27-video analysis group, overall mAPs were 86.8% and 71.2%, with maximum mAPs of 98.1% and 93.9%. We found commonalities between the error of model recognition and the differences of surgeon annotation, and the AI model exhibited bad performance in cases with anatomic variation or lesion involvement with adjacent organs.
CONCLUSIONS: AI automated surgical phase recognition can be achieved in LPD, with outstanding performance in selective cases. This breakthrough may be the first step toward AI- and video-based surgical education in more complex surgeries.

暂无摘要。

UNASSIGNED: Large language model (LLM)-powered services are gaining popularity in various applications due to their exceptional performance in many tasks, such as sentiment analysis and answering questions. Recently, research has been exploring their potential use in digital health contexts, particularly in the mental health domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, and clinical challenges. In this viewpoint paper, we discuss 2 challenges that affect the use of LLM-enhanced CAI for individuals with mental health issues, focusing on the use case of patients with depression: the tendency to humanize LLM-enhanced CAI and their lack of contextualized robustness. Our approach is interdisciplinary, relying on considerations from philosophy, psychology, and computer science. We argue that the humanization of LLM-enhanced CAI hinges on the reflection of what it means to simulate \"human-like\" features with LLMs and what role these systems should play in interactions with humans. Further, ensuring the contextualization of the robustness of LLMs requires considering the specificities of language production in individuals with depression, as well as its evolution over time. Finally, we provide a series of recommendations to foster the responsible design and deployment of LLM-enhanced CAI for the therapeutic support of individuals with depression.

BACKGROUND: Artificial intelligence (AI) is becoming an important part of our lives owing to increased data availability and improved power of computing. One of the recently launched modalities of AI, ChatGPT, is being enormously used worldwide for different types of tasks. In medical context, its use is being explored for clinical queries, academia, research help, etc. Further, literature suggests that parents seek information about health of their children using different Internet resources and would surely turn toward ChatGPT for the same, as this chatbot model is easy to use, generates \"one\" response, and is available without any subscription. ChatGPT generates a response using text cues and applying different algorithms on prepublished literature but is still in its naïve state; hence, it is imperative to validate the generated responses. Accordingly, we planned this study to determine the clarity, correctness, and completeness of some Frequently asked questions (FAQs) about child\'s oral health, from a mother\'s perspective.
METHODS: The study design was a vignette-based survey and included a set of 23 questions, for which ChatGPT was interviewed from the perspective of an imaginary parent. The answers responded by ChatGPT were copied \"verbatim,\" and a Google survey form was designed. The survey form was validated and then sent to 15 pediatric dentists, and the responses were mainly collected on the Likert\'s scale with a provision of one open-ended question aiming to determine \"what they would have added\" to this generated response as an expert in the field.
RESULTS: The responses on Likert\'s scale were condensed and values ≥4 were considered \'adequate and acceptable\' while scores ≤3, were considered \'inadequate\'. The generated responses and comments mentioned by different respondents in the open-ended question were critiqued in reference to the existing literature.
CONCLUSIONS: Overall, the responses were found to be complete and logical and in clear language, with only some inadequacies being reported in few of the answers.

UNASSIGNED: Missed diagnosis of evolving or coexisting idiopathic (IIH) and spontaneous intracranial hypotension (SIH) is often the reason for persistent or worsening symptoms after foramen magnum decompression for Chiari malformation (CM) I. We explore the role of artificial intelligence (AI)/convolutional neural networks (CNN) in Chiari I malformation in a combinatorial role for the first time in literature, exploring both upstream and downstream magnetic resonance findings as initial screening profilers in CM-1. We have also put together a review of all existing subtypes of CM and discuss the role of upright (gravity-aided) magnetic resonance imaging (MRI) in evaluating equivocal tonsillar descent on a lying-down MRI. We have formulated a workflow algorithm MaChiP 1.0 (Manjila Chiari Protocol 1.0) using upstream and downstream profilers, that cause de novo or worsening Chiari I malformation, which we plan to implement using AI.
UNASSIGNED: The PRISMA guidelines were used for \"CM and machine learning and CNN\" on PubMed database articles, and four articles specific to the topic were encountered. The radiologic criteria for IIH and SIH were applied from neurosurgical literature, and they were applied between primary and secondary (acquired) Chiari I malformations. An upstream etiology such as IIH or SIH and an isolated downstream etiology in the spine were characterized using the existing body of literature. We propose the utility of using four selected criteria for IIH and SIH each, over MRI T2 images of the brain and spine, predominantly sagittal sequences in upstream etiology in the brain and multiplanar MRI in spinal lesions.
UNASSIGNED: Using MaChiP 1.0 (patent/ copyright pending) concepts, we have proposed the upstream and downstream profilers implicated in progressive Chiari I malformation. The upstream profilers included findings of brain sagging, slope of the third ventricular floor, pontomesencephalic angle, mamillopontine distance, lateral ventricular angle, internal cerebral vein-vein of Galen angle, and displacement of iter, clivus length, tonsillar descent, etc., suggestive of SIH. The IIH features noted in upstream pathologies were posterior flattening of globe of the eye, partial empty sella, optic nerve sheath distortion, and optic nerve tortuosity in MRI. The downstream etiologies involved spinal cerebrospinal fluid (CSF) leak from dural tear, meningeal diverticula, CSF-venous fistulae, etc.
UNASSIGNED: AI would help offer predictive analysis along the spectrum of upstream and downstream etiologies, ensuring safety and efficacy in treating secondary (acquired) Chiari I malformation, especially with coexisting IIH and SIH. The MaChiP 1.0 algorithm can help document worsening of a previously diagnosed CM-1 and find the exact etiology of a secondary CM-I. However, the role of posterior fossa morphometry and cine-flow MRI data for intracranial CSF flow dynamics, along with advanced spinal CSF studies using dynamic myelo-CT scanning in the formation of secondary CM-I is still being evaluated.

UNASSIGNED: The aim of this study was to assess the completeness and readability of generative pre-trained transformer-4 (GPT-4)-generated discharge instructions at prespecified reading levels for common pediatric emergency room complaints.
UNASSIGNED: The outputs for 6 discharge scenarios stratified by reading level (fifth or eighth grade) and language (English, Spanish) were generated fivefold using GPT-4. Specifically, 120 discharge instructions were produced and analyzed (6 scenarios: 60 in English, 60 in Spanish; 60 at a fifth-grade reading level, 60 at an eighth-grade reading level) and compared for completeness and readability (between language, between reading level, and stratified by group and reading level). Completeness was defined as the proportion of literature-derived key points included in discharge instructions. Readability was quantified using Flesch-Kincaid (English) and Fernandez-Huerta (Spanish) readability scores.
UNASSIGNED: English-language GPT-generated discharge instructions contained a significantly higher proportion of must-include discharge instructions than those in Spanish (English: mean (standard error of the mean) = 62% (3%), Spanish: 53% (3%), P = .02). In the fifth-grade and eighth-grade level conditions, there was no significant difference between English and Spanish outputs in completeness. Readability did not differ across languages.
UNASSIGNED: GPT-4 produced readable discharge instructions in English and Spanish while modulating document reading level. Discharge instructions in English tended to have higher completeness than those in Spanish.
UNASSIGNED: Future research in prompt engineering and GPT-4 performance, both generally and in multiple languages, is needed to reduce potential for health disparities by language and reading level.

暂无摘要。

Quantitative analysis of the dynamic properties of thoraco-abdominal organs such as lungs during respiration could lead to more accurate surgical planning for disorders such as Thoracic Insufficiency Syndrome (TIS). This analysis can be done from semi-automatic delineations of the aforesaid organs in scans of the thoraco-abdominal body region. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for this application, although automatic segmentation of the organs in these images is very challenging. In this paper, we describe an auto-segmentation system we built and evaluated based on dMRI acquisitions from 95 healthy subjects. For the three recognition approaches, the system achieves a best average location error (LE) of about 1 voxel for the lungs. The standard deviation (SD) of LE is about 1-2 voxels. For the delineation approach, the average Dice coefficient (DC) is about 0.95 for the lungs. The standard deviation of DC is about 0.01 to 0.02 for the lungs. The system seems to be able to cope with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data and on other thoraco-abdominal organs including liver, kidneys, and spleen.