OBJECTIVE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline.
METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline.
RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%).
CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

Blood cell morphological examination is a crucial method for the diagnosis of blood diseases, but traditional manual microscopy is characterized by low efficiency and susceptibility to subjective biases. The application of artificial intelligence (AI) technology has improved the efficiency and quality of blood cell examinations and facilitated the standardization of test results. Currently, a variety of AI devices are either in clinical use or under research, with diverse technical requirements and configurations. The Experimental Diagnostic Study Group of the Hematology Branch of the Chinese Medical Association has organized a panel of experts to formulate this consensus. The consensus covers term definitions, scope of application, technical requirements, clinical application, data management, and information security. It emphasizes the importance of specimen preparation, image acquisition, image segmentation algorithms, and cell feature extraction and classification, and sets forth basic requirements for the cell recognition spectrum. Moreover, it provides detailed explanations regarding the fine classification of pathological cells, requirements for cell training and testing, quality control standards, and assistance in issuing diagnostic reports by humans. Additionally, the consensus underscores the significance of data management and information security to ensure the safety of patient information and the accuracy of data.
血细胞形态学检查是血液疾病诊断的重要手段，传统人工镜检存在效率低、易受主观影响等问题。人工智能技术的应用提高了血细胞检查的效率和质量，促进了检测结果的标准化。目前，多种人工智能设备已在临床使用或研究中，但其技术要求和配置各异。中华医学会血液学分会实验诊断学组组织相关专家，制订了本共识。本共识内容包括术语定义、适用范围、技术要求、临床应用、数据管理和信息安全，强调了标本制备、图像采集、图像分割算法、细胞特征提取与识别分类的重要性，并提出了细胞识别谱的基本要求。同时，对病理细胞的细分类、细胞训练与测试的要求、质量控制标准、辅助人工出具诊断报告等方面进行了详细说明。此外，还强调了数据管理和信息安全的重要性，确保患者信息的安全和数据的准确性。.

Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside.

UNASSIGNED: Stress urinary incontinence (SUI) affects countless women worldwide. Given ChatGPT\'s rising ubiquity, patients may turn to the platform for SUI advice. Our objective was to evaluate the quality of clinical information about SUI from the ChatGPT platform.
UNASSIGNED: The most-asked patient questions regarding SUI were derived from patient materials from societal websites and forums, and queried using ChatGPT 3.5. The responses from ChatGPT were compiled into a survey and disseminated to 3 AUA guideline committee members who developed the Surgical Management of Female SUI guidelines. They were asked to grade responses on reliability, understandability, quality, and actionability using DISCERN and Patient Education Materials Assessment Tool standardized questionnaires. Accuracy was assessed with a 4-point Likert scale and readability using Flesch Reading Ease score.
UNASSIGNED: The overall material was rated as moderate to moderately high quality (DISCERN = 3.73/5) with potentially important but no serious shortcomings. Reliability and quality were reported to be 63% and 75%. Understandability was 89%, actionability 18%, and accuracy 88%. All question domains were rated at moderate or better. Actionability was poor in all domains. Every response was \"hard to read\" translating to a college graduate reading level.
UNASSIGNED: The urologic community should critically evaluate this platform\'s output if patients are to use it for adjunctive medical guidance. AUA committee members, who are experts in the field, rate ChatGPT-produced responses on SUI as moderate to moderately high quality, moderate reliability, excellent understandability, and poor actionability utilizing standardized questionnaires. The reading level of the material was advanced, which is an area of potential improvement to make generated responses more comprehensible.

In a recent Letter to the Editor authored by Daungsupawong et al. in Aesthetic Plastic Surgery, titled \"ChatGPT and Clinical Questions on the Practical Guideline of Blepharoptosis: Correspondence,\" the authors emphasized important points regarding the input language differences between input and output references. However, advanced versions, such as GPT-4, have shown marginal differences between English and Chinese inputs, possibly because of the use of larger training data. To address this issue, non-English-language-oriented large language models (LLMs) have been developed. The ability of LLMs to refer to existing references varies, with newer models, such as GPT-4, showing higher reference rates than GPT-3.5. Future research should focus on addressing the current limitations and enhancing the effectiveness of emerging LLMs in providing accurate and informative answers to medical questions across multiple languages.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Background: When properly utilized, artificial intelligence generated content (AIGC) may improve virtually every aspect of research, from data gathering to synthesis. Nevertheless, when used inappropriately, the use of AIGC may lead to the dissemination of inaccurate information and introduce potential ethical concerns.Research Design: Cross-sectional. Study Sample: 65 top surgical journals. Data Collection: Each journals submission guidelines and portal was queried for guidelines regarding AIGC use.Results: We found that, in July 2023, 60% of the top 65 surgical journals had introduced guidelines for use, with more surgical journals (68%) introducing guidelines than surgical subspecialty journals (52.5%), including otolaryngology (40%). Furthermore, of the 39 with guidelines, only 69.2% gave specific use guidelines. No included journal, at the time of analysis, explicitly disallowed AIGC use.Conclusions: Altogether, this data suggests that while many journals have quickly reacted to AIGC usage, the quality of such guidelines is still variable. This should be pre-emptively addressed within academia.

OBJECTIVE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.
METHODS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended.
RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated.
CONCLUSIONS: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI\'s beneficial outputs and concerns about the challenges of human-computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.

In recent years, generative Artificial Intelligence models, such as ChatGPT, have increasingly been utilized in healthcare. Despite acknowledging the high potential of AI models in terms of quick access to sources and formulating responses to a clinical question, the results obtained using these models still require validation through comparison with established clinical guidelines. This study compares the responses of the AI model to eight clinical questions with the Italian Association of Medical Oncology (AIOM) guidelines for ovarian cancer.
The authors used the Delphi method to evaluate responses from ChatGPT and the AIOM guidelines. An expert panel of healthcare professionals assessed responses based on clarity, consistency, comprehensiveness, usability, and quality using a five-point Likert scale. The GRADE methodology assessed the evidence quality and the recommendations\' strength.
A survey involving 14 physicians revealed that the AIOM guidelines consistently scored higher averages compared to the AI models, with a statistically significant difference. Post hoc tests showed that AIOM guidelines significantly differed from all AI models, with no significant difference among the AI models.
While AI models can provide rapid responses, they must match established clinical guidelines regarding clarity, consistency, comprehensiveness, usability, and quality. These findings underscore the importance of relying on expert-developed guidelines in clinical decision-making and highlight potential areas for AI model improvement.

OBJECTIVE: Severe macrovesicular steatosis in donor livers is associated with primary graft dysfunction. The Banff Working Group on Liver Allograft Pathology has proposed recommendations for steatosis assessment of donor liver biopsy specimens with a consensus for defining \"large droplet fat\" (LDF) and a 3-step algorithmic approach.
METHODS: We retrieved slides and initial pathology reports from potential liver donor biopsy specimens from 2010 to 2021. Following the Banff approach, we reevaluated LDF steatosis and employed a computer-assisted manual quantification protocol and artificial intelligence (AI) model for analysis.
RESULTS: In a total of 113 slides from 88 donors, no to mild (<33%) macrovesicular steatosis was reported in 88.5% (100/113) of slides; 8.8% (10/113) was reported as at least moderate steatosis (≥33%) initially. Subsequent pathology evaluation, following the Banff recommendation, revealed that all slides had LDF below 33%, a finding confirmed through computer-assisted manual quantification and an AI model. Correlation coefficients between pathologist and computer-assisted manual quantification, between computer-assisted manual quantification and the AI model, and between the AI model and pathologist were 0.94, 0.88, and 0.81, respectively (P < .0001 for all).
CONCLUSIONS: The 3-step approach proposed by the Banff Working Group on Liver Allograft Pathology may be followed when evaluating steatosis in donor livers. The AI model can provide a rapid and objective assessment of liver steatosis.