Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

Artificial intelligence: Friend or foe?

The brave new world of artificial intelligence: dawn of a new era

The use of artificial intelligence (AI) and machine learning (ML) in healthcare has the potential to revolutionize the way in which patients are diagnosed, treated, and monitored. The ability of AI and ML algorithms to process and analyse large amounts of data has led to the development of new diagnostic and treatment tools that can improve patient outcomes. However, the use of these technologies in healthcare is still in its infancy, and there is a need for further research to fully understand their potential impact. Recent studies have shown that AI can improve diagnostic accuracy in a variety of medical fields, including radiology, pathology, and dermatology (Hashmi, 2017; Schüffler, 2016). In radiology, for example, deep learning algorithms have been used to analyse medical images, such as mammograms and CT scans, with a level of accuracy that is comparable to that of human radiologists (Yang, 2018). In pathology, AI algorithms have been used to analyse medical images, such as biopsy slides, with a level of accuracy that is comparable to that of human pathologists (Thrall, 2018). Furthermore, AI and ML have the potential to improve patient outcomes by identifying high-risk patients and providing personalized treatment plans. For example, machine learning algorithms have been used to predict the risk of readmission in patients with heart failure (Murphy, 2020). This can help to identify patients who are at high risk for readmission and provide them with targeted interventions to prevent readmission. Despite the potential benefits of AI and ML in healthcare, there are also potential challenges and limitations that need to be considered. These include issues related to data privacy and security, as well as concerns about the potential impact of these technologies on healthcare workforce (Hashmi, 2017). In conclusion, the use of AI and ML in healthcare has the potential to revolutionize the way in which patients are diagnosed, treated, and monitored. However, further research is needed to fully understand the potential impact of these technologies on patient outcomes and to address potential challenges and limitations.

BackgroundPoor reporting quality in diagnostic accuracy studies hampers an adequate judgment of the validity of the study. The Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement was published to improve the reporting quality of diagnostic accuracy studies. This study aimed to evaluate the adherence of diagnostic accuracy studies published in Annals of Laboratory Medicine (ALM) to STARD 2015 and to identify directions for improvement in the reporting quality of these studies.MethodsTwo independent authors assessed articles published in ALM between 2012–2018 for compliance with 30 STARD 2015 checklist items to identify all eligible diagnostic accuracy studies published during this period. We included 66 diagnostic accuracy studies. A total of the fulfilled STARD items were calculated, and adherence was analyzed on an individual-item basis.ResultsThe overall mean±SD number of STARD items reported for the included studies was 11.2±2.7. Only five (7.6%) studies adhered to more than 50% of the 30 items. No study satisfied more than 80% of the items. Large variability in adherence to reporting standards was detected across items, ranging from 0% to 100%.ConclusionsAdherence to STARD 2015 is suboptimal among diagnostic accuracy studies published in ALM. Our study emphasizes the necessity of adherence to STARD to improve the reporting quality of future diagnostic accuracy studies to be published in ALM.

The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non-peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher's Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.

To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0-100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The AUROC of the developed three AI algorithms (0.955-0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892-0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963-0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001). AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.

Rationale and Objectives: Accurate diagnosis and staging of cervical precancers is essential for practical medicine in determining the extent of the lesion extension and determines the most correct and effective therapeutic approach. For accurate diagnosis and staging of cervical precancers, we aim to create a diagnostic method optimized by artificial intelligence (AI) algorithms and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation of colposcopy images and improve therapeutic planning. Materials and Methods: The optimization of the method will consist in the development and formation of artificial intelligence models, using complicated convolutional neural networks (CNN) to identify precancers and cancers on colposcopic images. We will use topologies that have performed well in similar image recognition projects, such as Visual Geometry Group Network (VGG16), Inception deep neural network with an architectural design that consists of repeating components referred to as Inception modules (Inception), deeply separable convolutions that significantly reduce the number of parameters (MobileNet) that is a class of Convolutional Neural Network (CNN), Return of investment for machine Learning (ROI), Fully Convolutional Network (U-Net) and Overcomplete Convolutional Network Kite-Net (KiU-Net). Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical precancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the computer assisted diagnostic (CAD) method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cervical precancers using AI. Conclusion: This diagnostic method, optimized by artificial intelligence algorithms and validated by the clinical trial, which we consider “second opinion”, improves the quality standard in diagnosing, staging and establishing therapeutic conduct in cervical precancer.

Poor reporting of diagnostic accuracy studies impedes an objective appraisal of the clinical performance of diagnostic tests. The Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement, first published in...

Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction

Challenges of artificial intelligence in medicine and dermatology

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and heterogeneous interstitial lung disease with a median survival of 2–5 years. Though the diagnosis has been improved due to newly published guidelines, the recognition of the prognosis of IPF remains a challenge. Recently, several studies attempted to build prognostic models by extracting predictive variates from pulmonary function data, basic information, or chest computed tomography (CT) and CT-derived parameters with clinical characteristics. Artificial intelligence (AI) algorithms, including principal component analysis, support vector machine, random survival forest, and convolutional neural network, could be applied to the procedure of IPF prognostic model, that is, region of interest extraction, image feature selection, clinical feature selection, and model construction. Compared to human visualization, AI algorithms show a higher efficiency in calculating and extracting deep features and a lower inter-observer variation. Thus, this review provides a comprehensive CT evaluation of IPF prognostic models and discusses the role of AI in constructing IPF prognostic models. The potential improvements of AI in CT assessments, including time-series CT analysis, optimization of AI algorithms, utilization of multi-modal data, and discovery of new biomarkers through unsupervised algorithms, could be introduced to make a more accurate and convenient assessment for the prognosis of IPF patients. This review describes the status quo and future direction of AI applications in CT analysis for prognostic models of IPF.Take home messageThe review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms and apply them to more clinical work.

Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography

Purpose This paper aims to explore the paradigm shift in leadership and strategic management driven by the integration of responsible artificial intelligence (AI) in healthcare. It explores the evolving role of leadership in adapting to AI technologies while ensuring ethical governance, transparency and accountability in healthcare decision-making. Design/methodology/approach This study conducts a comprehensive review of current literature, case studies and industry reports to evaluate the implications of responsible AI adoption in healthcare leadership. It focuses on key areas such as AI-driven decision-making, resource optimisation, crisis management and patient care, while also addressing challenges in integrating AI technologies effectively. Findings The integration of AI in healthcare is transforming leadership from traditional, experience-based decision-making to data-driven, AI-enhanced strategies. Responsible leadership emphasises addressing ethical concerns such as bias, transparency and accountability. AI technologies improve resource allocation, crisis management and patient care, but challenges such as workforce resistance and the need for upskilling healthcare professionals remain. Practical implications Healthcare leaders must adopt a responsible leadership framework that balances AI’s potential with ethical and human-centred care principles. Recommendations include developing AI literacy programmes for healthcare professionals, ensuring inclusivity in AI algorithms and establishing governance policies that promote transparency and accountability in AI applications. Originality/value This paper provides a critical, forward-looking perspective on how responsible AI can drive a paradigm shift in healthcare leadership. It offers novel insights into the integration of AI within healthcare organisations, emphasising the need for leadership that prioritises ethical AI usage and promotes patient well-being in a rapidly evolving digital landscape.

ObjectiveTo determine the rate with which diagnostic test accuracy studies that are published in a general radiology journal adhere to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015, and to explore the relationship between adherence rate and citation rate while avoiding confounding by journal factors.Materials and MethodsAll eligible diagnostic test accuracy studies that were published in the Korean Journal of Radiology in 2011–2015 were identified. Five reviewers assessed each article for yes/no compliance with 27 of the 30 STARD 2015 checklist items (items 28, 29, and 30 were excluded). The total STARD score (number of fulfilled STARD items) was calculated. The score of the 15 STARD items that related directly to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 was also calculated. The number of times each article was cited (as indicated by the Web of Science) after publication until March 2016 and the article exposure time (time in months between publication and March 2016) were extracted.ResultsSixty-three articles were analyzed. The mean (range) total and QUADAS-2-related STARD scores were 20.0 (14.5–25) and 11.4 (7–15), respectively. The mean citation number was 4 (0–21). Citation number did not associate significantly with either STARD score after accounting for exposure time (total score: correlation coefficient = 0.154, p = 0.232; QUADAS-2-related score: correlation coefficient = 0.143, p = 0.266).ConclusionThe degree of adherence to STARD 2015 was moderate for this journal, indicating that there is room for improvement. When adjusted for exposure time, the degree of adherence did not affect the citation rate.

From black box to clarity: Strategies for effective AI informed consent in healthcare.