Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study

Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis

BACKGROUND

Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics.

OBJECTIVES

To critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL.

METHODS

Ovid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824.

RESULTS

2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77-86), SPE was 87 % (range 84-90), with an AUC of 0.92 (range 0.89-0.94). Human-machine comparison had pooled AUCs of 0.87 (0.84-0.90) and 0.83 (0.79-0.86) for DL and dermatologists, respectively. Pooled AUCs were 0.90 (0.87-0.93), 0.80 (0.76-0.83), and 0.88 (0.85-0.91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0.88 (0.85-0.91) for DL, 0.76 (0.72-0.79) for unassisted, and 0.87 (0.84-0.90) for DL-assisted dermatologists.

CONCLUSIONS

Evidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.

Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis

PURPOSE

To evaluate the diagnostic accuracy of artificial intelligence (AI)-based automated diabetic retinopathy (DR) screening in real-world settings.

DESIGN

Systematic review and meta-analysis METHODS: We conducted a systematic review of relevant literature from January 2012 to August 2022 using databases including PubMed, Scopus and Web of Science. The quality of studies was evaluated using Quality Assessment for Diagnostic Accuracy Studies 2 (QUADAS-2) checklist. We calculated pooled accuracy, sensitivity, specificity, and diagnostic odds ratio (DOR) as summary measures. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO - CRD42022367034).

RESULTS

We included 34 studies which utilized AI algorithms for diagnosing DR based on real-world fundus images. Quality assessment of these studies indicated a low risk of bias and low applicability concern. Among gradable images, the overall pooled accuracy, sensitivity, specificity, and DOR were 81%, 94% (95% CI: 92.0-96.0), 89% (95% CI: 85.0-92.0) and 128 (95% CI: 80-204) respectively. Sub-group analysis showed that, when acceptable quality imaging could be obtained, non-mydriatic fundus images had a better DOR of 143 (95% CI: 82-251) and studies using 2 field images had a better DOR of 161 (95% CI 74-347). Our meta-regression analysis revealed a statistically significant association between DOR and variables such as the income status, and the type of fundus camera.

CONCLUSION

Our findings indicate that AI algorithms have acceptable performance in screening for DR using fundus images compared to human graders. Implementing a fundus camera with AI-based software has the potential to assist ophthalmologists in reducing their workload and improving the accuracy of DR diagnosis.

Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights

Artificial intelligence (AI) is increasingly being used in the medical field, specifically for brain cancer imaging. In this review, we explore how AI-powered medical imaging can impact the diagnosis, prognosis, and treatment of brain cancer. We discuss various AI techniques, including deep learning and causality learning, and their relevance. Additionally, we examine current applications that provide practical solutions for detecting, classifying, segmenting, and registering brain tumors. Although challenges such as data quality, availability, interpretability, transparency, and ethics persist, we emphasise the enormous potential of intelligent applications in standardising procedures and enhancing personalised treatment, leading to improved patient outcomes. Innovative AI solutions have the power to revolutionise neuro-oncology by enhancing the quality of routine clinical practice.

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

OBJECTIVES

This systematic review and meta-analysis aimed to assess the stroke detection performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and additionally to identify reporting insufficiencies.

METHODS

PRISMA guidelines were followed. MEDLINE, Embase, Cochrane Central, and IEEE Xplore were searched for studies utilising MRI and AI for stroke detection. The protocol was prospectively registered with PROSPERO (CRD42021289748). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were the primary outcomes. Only studies using MRI in adults were included. The intervention was AI for stroke detection with ischaemic and haemorrhagic stroke in separate categories. Any manual labelling was used as a comparator. A modified QUADAS-2 tool was used for bias assessment. The minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist was used to assess reporting insufficiencies. Meta-analyses were performed for sensitivity, specificity, and hierarchical summary ROC (HSROC) on low risk of bias studies.

RESULTS

Thirty-three studies were eligible for inclusion. Fifteen studies had a low risk of bias. Low-risk studies were better for reporting MI-CLAIM items. Only one study examined a CE-approved AI algorithm. Forest plots revealed detection sensitivity and specificity of 93% and 93% with identical performance in the HSROC analysis and positive and negative likelihood ratios of 12.6 and 0.079.

CONCLUSION

Current AI technology can detect ischaemic stroke in MRI. There is a need for further validation of haemorrhagic detection. The clinical usability of AI stroke detection in MRI is yet to be investigated.

CRITICAL RELEVANCE STATEMENT

This first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials.

KEY POINTS

There is a growing interest in AI solutions for detection aid. The performance is unknown for MRI stroke assessment. AI detection sensitivity and specificity were 93% and 93% for ischaemic lesions. There is limited evidence for the detection of patients with haemorrhagic lesions. AI can accurately detect patients with ischaemic stroke in MRI.

The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review

The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need for innovative solutions. Artificial intelligence (AI) holds promise for improving the diagnostic analysis of skin lesion images, potentially enhancing patient care in primary settings. This systematic review following PRISMA guidelines examined primary studies (2012-2022) assessing AI algorithms' diagnostic accuracy for skin diseases in primary care. Studies were screened for eligibility based on their availability in the English language and exclusion criteria, with risk of bias evaluated using QUADAS-2. PubMed, Scopus, and Web of Science were searched. Fifteen studies (2019-2022), primarily from Europe and the USA, focusing on diagnostic accuracy were included. Sensitivity ranged from 58% to 96.1%, with accuracies varying from 0.41 to 0.93. AI applications encompassed triage and diagnostic support across diverse skin conditions in primary care settings, involving both patients and primary care professionals. While AI demonstrates potential for enhancing the accuracy of skin disease diagnostics in primary care, further research is imperative to address study heterogeneity and ensure algorithm reliability across diverse populations. Future investigations should prioritise robust dataset development and consider representative patient samples. Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.

Application and Prospects of Artificial Intelligence Technology in Early Screening of Chronic Obstructive Pulmonary Disease at Primary Healthcare Institutions in China

Chronic Obstructive Pulmonary Disease (COPD), as one of the major global health threat diseases, particularly in China, presents a high prevalence and mortality rate. Early diagnosis is crucial for controlling disease progression and improving patient prognosis. However, due to the lack of significant early symptoms, the awareness and diagnosis rates of COPD remain low. Against this background, primary healthcare institutions play a key role in identifying high-risk groups and early diagnosis. With the development of Artificial Intelligence (AI) technology, its potential in enhancing the efficiency and accuracy of COPD screening is evident. This paper discusses the characteristics of high-risk groups for COPD, current screening methods, and the application of AI technology in various aspects of screening. It also highlights challenges in AI application, such as data privacy, algorithm accuracy, and interpretability. Suggestions for improvement, such as enhancing AI technology dissemination, improving data quality, promoting interdisciplinary cooperation, and strengthening policy and financial support, aim to further enhance the effectiveness and prospects of AI technology in COPD screening at primary healthcare institutions in China.

Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis

BACKGROUND

The field of Artificial Intelligence (AI) holds transformative potential in medicine. However, the lack of universal reporting guidelines poses challenges in ensuring the validity and reproducibility of published research studies in this field.

METHODS

Based on a systematic review of academic publications and reporting standards demanded by both international consortia and regulatory stakeholders as well as leading journals in the fields of medicine and medical informatics, 26 reporting guidelines published between 2009 and 2023 were included in this analysis. Guidelines were stratified by breadth (general or specific to medical fields), underlying consensus quality, and target research phase (preclinical, translational, clinical) and subsequently analyzed regarding the overlap and variations in guideline items.

RESULTS

AI reporting guidelines for medical research vary with respect to the quality of the underlying consensus process, breadth, and target research phase. Some guideline items such as reporting of study design and model performance recur across guidelines, whereas other items are specific to particular fields and research stages.

CONCLUSIONS

Our analysis highlights the importance of reporting guidelines in clinical AI research and underscores the need for common standards that address the identified variations and gaps in current guidelines. Overall, this comprehensive overview could help researchers and public stakeholders reinforce quality standards for increased reliability, reproducibility, clinical validity, and public trust in AI research in healthcare. This could facilitate the safe, effective, and ethical translation of AI methods into clinical applications that will ultimately improve patient outcomes.

Artificial intelligence in diagnosing upper limb musculoskeletal disorders: a systematic review and meta-analysis of diagnostic tests

Purpose

The integration of artificial intelligence (AI) in radiology has revolutionized diagnostics, optimizing precision and decision-making. Specifically in musculoskeletal imaging, AI tools can improve accuracy for upper extremity pathologies. This study aimed to assess the diagnostic performance of AI models in detecting musculoskeletal pathologies of the upper extremity using different imaging modalities.

Methods

A meta-analysis was conducted, involving searches on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The quality of the studies was assessed using the QUADAS-2 tool. Diagnostic accuracy measures including sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR, NLR), area under the curve (AUC), and summary receiver operating characteristic were pooled using a random-effects model. Heterogeneity and subgroup analyses were also included. All statistical analyses and plots were performed using the R software package.

Results

Thirteen models from ten articles were analyzed. The sensitivity and specificity of the AI models to detect musculoskeletal conditions in the upper extremity were 0.926 (95% CI: 0.900; 0.945) and 0.908 (95% CI: 0.810; 0.958). The PLR, NLR, lnDOR, and the AUC estimates were found to be 19.18 (95% CI: 8.90; 29.34), 0.11 (95% CI: 0.18; 0.46), 4.62 (95% CI: 4.02; 5.22) with a (P < 0.001), and 95%, respectively.

Conclusion

The AI models exhibited strong univariate and bivariate performance in detecting both positive and negative cases within the analyzed dataset of musculoskeletal pathologies in the upper extremity.

Artificial Intelligence in the Diagnosis and Management of Appendicitis in Pediatric Departments: A Systematic Review

INTRODUCTION

 Artificial intelligence (AI) is a growing field in medical research that could potentially help in the challenging diagnosis of acute appendicitis (AA) in children. However, usefulness of AI in clinical settings remains unclear. Our aim was to assess the accuracy of AIs in the diagnosis of AA in the pediatric population through a systematic literature review.

METHODS

 PubMed, Embase, and Web of Science were searched using the following keywords: "pediatric," "artificial intelligence," "standard practices," and "appendicitis," up to September 2023. The risk of bias was assessed using PROBAST.

RESULTS

 A total of 302 articles were identified and nine articles were included in the final review. Two studies had prospective validation, seven were retrospective, and no randomized control trials were found. All studies developed their own algorithms and had an accuracy greater than 90% or area under the curve >0.9. All studies were rated as a "high risk" concerning their overall risk of bias.

CONCLUSION

 We analyzed the current status of AI in the diagnosis of appendicitis in children. The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review

INTRODUCTION

A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to those of certified dentists. This methodological systematic review aimed to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning that have used radiographic databases to classify, detect, and segment dental caries.

METHODS

The protocol was registered in PROSPERO before data collection (CRD42022348097). Literature research was performed in MEDLINE, Embase, IEEE Xplore, and Web of Science until December 2022, without language restrictions. Studies and surveys using a dental radiographic database for the classification, detection, or segmentation of carious lesions were sought. Records deemed eligible were retrieved and further assessed for inclusion by two reviewers who resolved any discrepancies through consensus. A third reviewer was consulted when any disagreements or discrepancies persisted between the two reviewers. After data extraction, the same reviewers assessed the methodological quality using the CLAIM and QUADAS-AI checklists.

RESULTS

After screening 325 articles, 35 studies were eligible and included. The bitewing was the most commonly used radiograph (n = 17) at the time when detection (n = 15) was the most explored computer vision task. The sample sizes used ranged from 95 to 38,437, while the augmented training set ranged from 300 to 315,786. Convolutional neural network was the most commonly used model. The mean completeness of CLAIM items was 49% (SD ± 34%). The applicability of the CLAIM checklist items revealed several weaknesses in the methodology of the selected studies: most of the studies were monocentric, and only 9% of them used an external test set when evaluating the model's performance. The QUADAS-AI tool revealed that only 43% of the studies included in this systematic review were at low risk of bias concerning the standard reference domain.

CONCLUSION

This review demonstrates that the overall scientific quality of studies conducted to feed artificial intelligence algorithms is low. Some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications.

Role of Ethics in Developing AI-Based Applications in Medicine: Insights From Expert Interviews and Discussion of Implications

BACKGROUND

The integration of artificial intelligence (AI)-based applications in the medical field has increased significantly, offering potential improvements in patient care and diagnostics. However, alongside these advancements, there is growing concern about ethical considerations, such as bias, informed consent, and trust in the development of these technologies.

OBJECTIVE

This study aims to assess the role of ethics in the development of AI-based applications in medicine. Furthermore, this study focuses on the potential consequences of neglecting ethical considerations in AI development, particularly their impact on patients and physicians.

METHODS

Qualitative content analysis was used to analyze the responses from expert interviews. Experts were selected based on their involvement in the research or practical development of AI-based applications in medicine for at least 5 years, leading to the inclusion of 7 experts in the study.

RESULTS

The analysis revealed 3 main categories and 7 subcategories reflecting a wide range of views on the role of ethics in AI development. This variance underscores the subjectivity and complexity of integrating ethics into the development of AI in medicine. Although some experts view ethics as fundamental, others prioritize performance and efficiency, with some perceiving ethics as potential obstacles to technological progress. This dichotomy of perspectives clearly emphasizes the subjectivity and complexity surrounding the role of ethics in AI development, reflecting the inherent multifaceted nature of this issue.

CONCLUSIONS

Despite the methodological limitations impacting the generalizability of the results, this study underscores the critical importance of consistent and integrated ethical considerations in AI development for medical applications. It advocates further research into effective strategies for ethical AI development, emphasizing the need for transparent and responsible practices, consideration of diverse data sources, physician training, and the establishment of comprehensive ethical and legal frameworks.

Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

BACKGROUND

The gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty liver.

METHODS

We searched the Cochrane Library, Pubmed, Embase and assessed the quality of included studies by QUADAS-AI. The pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated using a random effects model. Summary receiver operating characteristic curves (SROC) were generated to identify the diagnostic accuracy of AI models.

RESULTS

15 studies were selected in our meta-analysis. Pooled sensitivity and specificity were 92% (95% CI: 90-93%) and 94% (95% CI: 93-96%), PLR and NLR were 12.67 (95% CI: 7.65-20.98) and 0.09 (95% CI: 0.06-0.13), DOR was 182.36 (95% CI: 94.85-350.61). After subgroup analysis by AI algorithm (conventional machine learning/deep learning), region, reference (US, MRI or pathology), imaging techniques (MRI or US) and transfer learning, the model also demonstrated acceptable diagnostic efficacy.

CONCLUSION

AI has satisfactory performance in the diagnosis of fatty liver by medical imaging. The integration of AI into imaging devices may produce effective diagnostic tools, but more high-quality studies are needed for further evaluation.

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment

Purpose

To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detection, diagnosis, prognosis, and risk assessment of disease with the goal of clinical implementation. These tools are intended to help improve traditional human decision-making in medical imaging. However, biases introduced in the steps toward clinical deployment may impede their intended function, potentially exacerbating inequities. Specifically, medical imaging AI can propagate or amplify biases introduced in the many steps from model inception to deployment, resulting in a systematic difference in the treatment of different groups.

Approach

Our multi-institutional team included medical physicists, medical imaging artificial intelligence/machine learning (AI/ML) researchers, experts in AI/ML bias, statisticians, physicians, and scientists from regulatory bodies. We identified sources of bias in AI/ML, mitigation strategies for these biases, and developed recommendations for best practices in medical imaging AI/ML development.

Results

Five main steps along the roadmap of medical imaging AI/ML were identified: (1) data collection, (2) data preparation and annotation, (3) model development, (4) model evaluation, and (5) model deployment. Within these steps, or bias categories, we identified 29 sources of potential bias, many of which can impact multiple steps, as well as mitigation strategies.

Conclusions

Our findings provide a valuable resource to researchers, clinicians, and the public at large.

Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers

Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as "consumers", "translators", or "developers". The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM). We outline a core curriculum for AI education of future consumers, translators, and developers, emphasizing the links between AI and EBM, with suggestions for how teaching may be integrated into existing curricula. We consider the key barriers to implementation of AI in the medical curriculum: time, resources, variable interest, and knowledge retention. By improving AI literacy rates and fostering a translator- and developer-enriched workforce, innovation may be accelerated for the benefit of patients and practitioners.

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review

BACKGROUND

Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma.

OBJECTIVE

The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma.

METHODS

A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives.

RESULTS

We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%.

CONCLUSIONS

Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.

Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.

Are current clinical studies on artificial intelligence-based medical devices comprehensive enough to support a full health technology assessment? A systematic review

INTRODUCTION

Artificial Intelligence-based Medical Devices (AI-based MDs) are experiencing exponential growth in healthcare. This study aimed to investigate whether current studies assessing AI contain the information required for health technology assessment (HTA) by HTA bodies.

METHODS

We conducted a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to extract articles published between 2016 and 2021 related to the assessment of AI-based MDs. Data extraction focused on study characteristics, technology, algorithms, comparators, and results. AI quality assessment and HTA scores were calculated to evaluate whether the items present in the included studies were concordant with the HTA requirements. We performed a linear regression for the HTA and AI scores with the explanatory variables of the impact factor, publication date, and medical specialty. We conducted a univariate analysis of the HTA score and a multivariate analysis of the AI score with an alpha risk of 5 %.

RESULTS

Of 5578 retrieved records, 56 were included. The mean AI quality assessment score was 67 %; 32 % of articles had an AI quality score ≥ 70 %, 50 % had a score between 50 % and 70 %, and 18 % had a score under 50 %. The highest quality scores were observed for the study design (82 %) and optimisation (69 %) categories, whereas the scores were lowest in the clinical practice category (23 %). The mean HTA score was 52 % for all seven domains. 100 % of the studies assessed clinical effectiveness, whereas only 9 % evaluated safety, and 20 % evaluated economic issues. There was a statistically significant relationship between the impact factor and the HTA and AI scores (both p = 0.046).

DISCUSSION

Clinical studies on AI-based MDs have limitations and often lack adapted, robust, and complete evidence. High-quality datasets are also required because the output data can only be trusted if the inputs are reliable. The existing assessment frameworks are not specifically designed to assess AI-based MDs. From the perspective of regulatory authorities, we suggest that these frameworks should be adapted to assess the interpretability, explainability, cybersecurity, and safety of ongoing updates. From the perspective of HTA agencies, we highlight that transparency, professional and patient acceptance, ethical issues, and organizational changes are required for the implementation of these devices. Economic assessments of AI should rely on a robust methodology (business impact or health economic models) to provide decision-makers with more reliable evidence.

CONCLUSION

Currently, AI studies are insufficient to cover HTA prerequisites. HTA processes also need to be adapted because they do not consider the important specificities of AI-based MDs. Specific HTA workflows and accurate assessment tools should be designed to standardise evaluations, generate reliable evidence, and create confidence.

Knowledge in Motion: A Comprehensive Review of Evidence-Based Human Kinetics

This comprehensive review examines critical aspects of evidence-based human kinetics, focusing on bridging the gap between scientific evidence and practical implementation. To bridge this gap, the development of tailored education and training programs is essential, providing practitioners with the expertise and skills to effectively apply evidence-based programs and interventions. The effectiveness of these programs in improving physical fitness across all age groups has been widely demonstrated. In addition, integrating artificial intelligence and the principles of slow science into evidence-based practice promises to identify gaps in knowledge and stimulate further research in human kinetics. The purpose of this review is to provide researchers and practitioners with comprehensive information on the application of scientific principles in human kinetics. By highlighting the importance of evidence-based practice, this review is intended to promote the adoption of effective interventions to optimize physical health and enhance performance.

Machine learning extracts oncogenic-specific γ-H2AX foci formation pattern upon genotoxic stress

H2AX is a histone H2A variant that becomes phosphorylated upon genotoxic stress. The phosphorylated H2AX (γ-H2AX) plays an antioncogenic role in the DNA damage response and its foci patterns are highly variable, in terms of intensities and sizes. However, whether characteristic γ-H2AX foci patterns are associated with oncogenesis (oncogenic-specific γ-H2AX foci patterns) remains unknown. We previously reported that a defect in the acetyltransferase activity of TIP60 promotes cancer cell growth in human cell lines. In this study, we compared γ-H2AX foci patterns between TIP60 wild-type cells and TIP60 HAT mutant cells by using machine learning. When focused solely on the intensity and size of γ-H2AX foci, we extracted the TIP60 HAT mutant-like oncogenic-specific γ-H2AX foci pattern among all datasets of γ-H2AX foci patterns. Furthermore, by using the dimensionality reduction method UMAP, we also observed TIP60 HAT mutant-like oncogenic-specific γ-H2AX foci patterns in TIP60 wild-type cells. In summary, we propose the existence of an oncogenic-specific γ-H2AX foci pattern and the importance of a machine learning approach to extract oncogenic signaling among the γ-H2AX foci variations.

Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?

Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012-2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012-2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [¹⁸F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([¹⁸F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [¹⁸F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [¹⁸F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [¹⁸F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

BACKGROUND

Majority of research and commercial efforts have focussed on use of artificial intelligence (AI) for fracture detection in adults, despite the greater long-term clinical and medicolegal implications of missed fractures in children. The objective of this study was to assess the available literature regarding diagnostic performance of AI tools for paediatric fracture assessment on imaging, and where available, how this compares with the performance of human readers.

MATERIALS AND METHODS

MEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011 and 2021 using terms related to 'fracture', 'artificial intelligence', 'imaging' and 'children'. Risk of bias was assessed using a modified QUADAS-2 tool. Descriptive statistics for diagnostic accuracies were collated.

RESULTS

Nine eligible articles from 362 publications were included, with most (8/9) evaluating fracture detection on radiographs, with the elbow being the most common body part. Nearly all articles used data derived from a single institution, and used deep learning methodology with only a few (2/9) performing external validation. Accuracy rates generated by AI ranged from 88.8 to 97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI were marginally higher, but this was not statistically significant.

CONCLUSIONS

Wide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools may generalise to a wider paediatric population. Further research using a multicentric dataset with real-world evaluation would help to better understand the impact of these tools.