Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?

Authors' response.

The integration of Artificial Intelligence (AI) in healthcare has been impeded by a significant issue: a lack of trust among healthcare professionals, stemming from the opacity of AI decision-making processes and a general unfamiliarity with AI technologies. This study investigates the impact of AI's explainability and healthcare professionals' familiarity with AI on their trust in AI applications within healthcare settings. Adopting a quantitative research methodology, the study utilized a structured questionnaire to gather data from a diverse group of healthcare professionals, including doctors, nurses, and administrators, across various hospitals and healthcare institutions in Pakistan. The research employed a stratified random sampling approach to ensure a comprehensive and representative data set. The results indicated a positive and significant relationship between AI explainability and trust in AI (Path Coefficient: 0.62, t-Value: 5.20), suggesting that clearer and more transparent AI decision-making processes enhance healthcare professionals' trust., Similarly, familiarity with AI was found to positively influence trust in AI (Path Coefficient: 0.48, t-Value: 4.35), highlighting the importance of exposure and understanding of AI systems among healthcare professionals. These findings have crucial implications for both AI developers and healthcare administrators. For AI developers, the emphasis must be on creating more transparent and interpretable AI systems. For healthcare administrators, the results suggest the need to invest in training and educational programs to increase professionals' familiarity with AI, thereby enhancing trust and acceptance. The study significantly contributes to the existing literature by empirically validating the importance of AI explainability and familiarity in building trust in AI within the healthcare context, especially in a developing country setting. For policymakers, these insights are critical in guiding strategies and policies aimed at effectively integrating AI into healthcare systems. By addressing the identified factors, healthcare sectors can better leverage AI's potential, leading to improved patient care and more efficient healthcare operations.

In this issue of The Lancet Digital Health, Xiaoxuan Liu and colleagues give their perspective on global auditing of medical artificial intelligence (AI). They call for the focus to shift from demonstrating the strengths of AI in health care to proactively discovering its weaknesses. Machines make unpredictable mistakes in medicine, which differ significantly from those made by humans. Liu and colleagues state that errors made by AI tools can have far-reaching consequences because of the complex and opaque relationships between the analysis and the clinical output. Given that there is little human control over how an AI generates results and that clinical knowledge is not a prerequisite in AI development, there is a risk of an AI learning spurious correlations that seem valid during training but are unreliable when applied to real-world situations. Lauren Oakden-Rayner and colleagues analysed the performance of an AI across a range of relevant features for hip fracture detection. This preclinical algorithmic audit identified barriers to clinical use, including a decrease in sensitivity at the prespecified operating point. This study highlighted several “failure modes”, which is the propensity of an AI to fail recurrently in certain conditions. Oakden-Rayner told The Lancet Digital Health that their study showed that “the failure modes of AI systems can look bizarre from a human perspective. Take, for example, in the hip fracture audit (figure 5), the recognition that the AI missed an extremely displaced fracture … the sort of image even a lay person would recognise as completely abnormal.” These errors can drastically affect clinician and patient trust in AI. Another example demonstrating the need for auditing was highlighted last month in an investigation by STAT and the Massachusetts Institute of Technology, which found that an EPIC health algorithm used to predict sepsis risk in the USA deteriorated sharply in performance, from 0·73 AUC to 0·53 AUC, over 10 years. This deterioration over time was caused by changes in the hospital coding system, increased diversity and volume of patient data, and changes in operational behaviours of caregivers. There was little to no oversight of the AI tool once it hit the market, potentially causing harm to patients in hospital. Liu commented, “without the ability to observe and learn from algorithmic errors, the risk is that it will continue to happen and there's no accountability for any harm that results.” Auditing medical AI is essential; but whose responsibility is it to ensure that AI is safe to use? Some experts think that AI developers are responsible for providing guidance on managing their tools, including how and when to check the system's performance, and identifying vulnerabilities that might emerge after they are put into practice. Others argue that not all the responsibility lies with AI developers, and health providers must test AI models on other data to verify their utility and assess potential vulnerabilities. Liu says, “we need clinical teams to start playing an active role in algorithmic safety oversight. They are best placed to define what success and failure looks like for their health institution and their patient cohort.” There are three challenges to overcome to ensure AI auditing is successfully implemented. First, in practice, auditing will require professionals with clinical and technical expertise to investigate and prevent AI errors and to thoughtfully interrogate errors before and during real-world deployment. However, experts with computational and clinical skill sets are not yet commonplace. Health-care institutes, AI companies, and governments must invest in upskilling health-care workers so that these experts can become an integral part of the medical AI development process. Second, industry-wide standards for monitoring medical AI tools over time must be enforced by key regulatory bodies. Tools to identify when an algorithm becomes miscalibrated because of changes in data or environment are being developed by researchers, but these tools must be endorsed in a sustained and standardised way, led by regulators, health systems, and AI developers. Third, the main issue that can exacerbate errors in AI is the lack of transparency of the data, code, and parameters due to intellectual property concerns. Liu and colleagues emphasise that much of the benefit that software and data access would provide can be instead obtained through a web portal with the ability to test the model on new data and receive model outputs. Oakden-Rayner said, “AI developers have a responsibility to make auditing easier for clinicians, especially by providing clear details of how their system works and how it was built.” The medical algorithmic auditArtificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. Full-Text PDF Open AccessValidation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy studyThe model outperformed the radiologists tested and maintained performance on external validation, but showed several unexpected limitations during further testing. Thorough preclinical evaluation of artificial intelligence models, including algorithmic auditing, can reveal unexpected and potentially harmful behaviour even in high-performance artificial intelligence systems, which can inform future clinical testing and deployment decisions. Full-Text PDF Open Access

Artificial intelligence (AI) has rapidly become one of the technologies used for competitive advantage. However, there are also growing concerns about bias in AI models as AI developers risk introducing bias both unintentionally and intentionally. This study, using a qualitative approach, investigated how AI developers can contribute to the development of fair AI models. The key findings reveal that the risk of bias is mainly because of the lack of gender and social diversity in AI development teams, and haste from AI managers to deliver much-anticipated results. The integrity of AI developers is also critical as they may conceal bias from management and other AI stakeholders. The testing phase before model deployment risks bias because it is rarely representative of the diverse societal groups that may be affected. The study makes practical recommendations in four main areas: governance, social, technical, and training and development processes. Responsible organisations need to take deliberate actions to ensure that their AI developers adhere to fair processes when developing AI; AI developers must prioritise ethical considerations and consider the impact their models may have on society; partnerships between AI developers, AI stakeholders, and society that might be impacted by AI models should be established; and AI developers need to prioritise transparency and explainability in their models while ensuring adequate testing for bias and corrective measures before deployment. Emotional intelligence training should also be provided to the AI developers to help them engage in productive conversations with individuals outside the development team.

AI users are not all alike: The characteristics of French firms buying and developing AI

This review aimed to explore the scientific literature concerning the methodologies and applications of artificial intelligence (AI) in the field of endodontics. The findings may equip dentists with the necessary technical knowledge to understand the opportunities presented by AI. Articles published between 1992 and 2023 were retrieved through an electronic search of Medline via the PubMed, Scopus, and Google Scholar databases. The search, which was limited to articles published in English, aimed to identify relevant studies by employing the following keywords: "artificial intelligence," "machine learning," "deep learning," "endodontic," "root canal treatment," and "radiography." Ultimately, 71 studies that addressed the application of AI in endodontics were selected. Numerous studies have demonstrated the effectiveness of AI applications in endodontics. These uses encompass the identification of root fractures and periapical lesions, assessment of working length, investigation of root canal system anatomy, prediction of retreatment success, and evaluation of dental pulp stem cell viability. AI technology is poised to advance aspects of endodontics including scheduling, patient care, management of drug-drug interactions, prognostic diagnosis, and the emerging area of robotic endodontic surgery. AI methods have demonstrated accuracy and precision in the identification, assessment, and prediction of diseases. Thus, AI can significantly improve endodontic diagnosis and treatment, increasing the overall efficacy of endodontic therapy.

Computer vision methods based on artificial intelligence (AI) have found numerous applications in endodontic diagnosis and treatment planning. While most current applications employ discriminative deep learning models for detection and classification tasks, the field is now witnessing the rise of generative AI (GenAI), a class of AI models that learn to generate new data samples resembling the original training data. Despite the potential of GenAI, its current state, applications in endodontics and challenges have not been thoroughly explored. This narrative review aims to explore the current state and potential applications of GenAI models in computer vision and imaging within endodontics. A literature search was conducted in PubMed/MEDLINE, Web of Science, Embase, Scopus and arXiv through July 2025, using predefined keywords related to generative models, dental imaging and endodontics. This comprehensive narrative review examines the fundamental principles and current applications of vision GenAI models in endodontics, aiming to clarify their capabilities and evaluate their prospective implications for future applications in endodontic diagnosis, image enhancement and treatment planning. Diverse applications of GenAI in endodontics were identified. Synthetic data generation models can produce unconditional and conditional dental images to augment training datasets and create educational content for rare pathologies. Image enhancement techniques, including super-resolution, denoising and artefact reduction, show promise in improving diagnostic quality by revealing fine anatomical details. Studies report improvements in quantitative metrics and clinical detection rates, particularly for challenging structures such as second mesiobuccal canals in maxillary molars. Modality conversion applications include 2D-to-3D reconstruction from radiographs, cone-beam computed tomography (CBCT) to magnetic resonance imaging conversion for enhanced soft tissue visualisation, and CBCT to micro-computed tomography transformation for improved spatial resolution. Treatment enhancement applications encompass customised surgical guide design, treatment outcome prediction and AI-based computer-aided design for restoration of endodontically treated teeth. However, current research is predominantly found in other fields of dentistry and emphasises technical feasibility rather than clinical validation in real-world scenarios. GenAI models are promising tools for enhancing endodontic treatments through data synthesis, image quality improvement and treatment optimisation. However, there are significant gaps between technical demonstrations and clinical implementation.

Artificial intelligence (AI) is rapidly transforming financial systems worldwide, particularly in Africa and Nigeria, by automating regulatory compliance, enhancing fraud detection, and improving risk management. However, integrating AI into financial regulations comes with both potential and challenges. This article examines the impact of AI-driven innovations on financial oversight, compliance systems, and regulatory frameworks in relation to Nigerian financial legislation. The report draws attention to a number of significant problems, such as possible biases in AI algorithms, moral dilemmas, threats to cyber security, legal gaps, and insufficient technology infrastructure. Additionally emphasized is the potential of artificial intelligence (AI) to enhance financial inclusion, increase regulatory efficacy, and enhance financial sector decision-making. Nigeria's present financial regulatory policies, the role of the Central Bank of Nigeria (CBN) and other financial regulatory agencies in adapting to AI developments, and global best practices that have the potential to alter Nigeria's regulatory landscape are all examined in this qualitative study. The paper ends with recommendations for a robust regulatory framework that promotes openness in AI, balances innovation and oversight, and strengthens collaboration between financial regulators, AI developers, and financial institutions. The results show that although artificial intelligence (AI) has many advantages, appropriate regulation is necessary to reduce related risks, guarantee ethical AI use, and safeguard financial stability

Artificial intelligence (AI) is revolutionizing various fields of dentistry, and endodontics is no exception. Endodontics, the branch of dentistry concerned with the study and treatment of dental pulp and the tissues surrounding the roots of a tooth, has been greatly influenced by advancements in AI technology. AI algorithms are being integrated into various aspects of endodontic practice, from diagnosis and treatment planning to procedural assistance and outcome prediction. One of the primary applications of AI in endodontics is in diagnostic imaging. AI-powered software can analyze radiographs and CBCT scans with remarkable accuracy, aiding in the detection of dental caries, periapical lesions, and anatomical variations. This not only improves diagnostic efficiency but also helps in treatment planning by providing clinicians with detailed insights into the patient's dental anatomy.

Artificial intelligence (AI) creators and developers attempt to simulate human thinking and our environment (e.g., popular web-based Second Life), but, controversially, claim to seek replicating the human brain (e.g., Human Brain Project) and what it does. However, some central questions are, "Who are we? Are we truly who we believe we are? What is thinking/mentation/ideas? Why is our world rife with contradictions and conflict? What is reality, itself?" Experts, like David Chalmers, refer to "consciousness" as the "hard problem". If we don't fully understand these concepts in humans, how can we possibly recreate them in AI? It's a puzzle, much like the challenge of defining and creating life itself. Answers are to be founded on what exists, not merely our desires. For replicating humans, AI developers must confront the difference between belief and authentic self. Beliefs can mask the true self. A major flaw of the well-known Turing Test—which assesses whether a machine can imitate human intelligence—is that it cannot verify whether someone's beliefs are reflected in their actions. AI developers must be competent technicians but integrate philosophy, thus addressing overlapping questions of meaning, ethics, purpose, and ethos. Even AI creators acknowledge AI could threaten humanity. If future technology integrates self-awareness or subjective experience into advanced computing systems, we will need to revisit some ancient wisdom, "Know thyself." Any viable human identity probe (such as Authentic Systems) must be underpinned by philosophy, thus revealing the extent to which one has internalized belief by action. One the material and psychological aspects of authentic identity are know, we apply the unity of opposites law to establish authentic human identity.

To evaluate M1 and M2 macrophage polarization in radicular cysts and periapical granulomas through an immunohistochemical analysis and the correlation between macrophage polarization and histopathological diagnosis, clinical characteristics and lesion volume using cone-beam computed tomography. Periapical biopsies diagnosed as radicular cysts (n = 52) and periapical granulomas (n = 51) were analysed by immunohistochemical method. Teeth with periapical lesion with no history of root canal treatment (primary lesion) and lesions persistent to root canal treatment (persistent lesions) were included. Pathological diagnosis, patients' age, gender and clinical characteristics were obtained from treatment records. A cone-beam computed tomographic periapical volume index (CBCTPAVI) score was assigned to each periapical lesion based on the volume of the lesion. Immuno-expressions of CD68 and CD163 were quantified. The CD68/CD163 ratio was adopted to represent M1 or M2 macrophage polarization. Mann-Whitney U test was used to determine the different CD68/CD163 ratio between groups of radicular cyst and periapical granuloma. Spearman's correlation test was performed to assess the correlation between the CD68/CD163 ratio and lesion volume and CBCTPAVI score. Radicular cysts and periapical granulomas had CD68/CD163 median of 2.05 (IQR = 1.33) and 1.26 (IQR = 0.81), respectively. A significantly higher CD68/CD163 ratio was observed in radicular cysts (p < .001). In contrast, periapical granulomas had significantly lower median of CD68/CD163 ratio. Larger lesions had a higher median of CD68/CD163 ratio, while smaller lesions had lower median of CD68/CD163 ratio (p = .007, rs = .262). CD68/CD163 ratio was significantly correlated with the CBCTPAVI score in the overall periapical lesions (p = .002, rs = .306). The higher CD68/CD163 ratio in larger lesions indicated a higher degree of M1 polarization compared to smaller lesions. Regarding the pathological diagnosis, there was a significant positive correlation between CBCTPAVI score and CD68/CD163 ratio in periapical granulomas (p < .001, rs = .453), whereas the negative correlation was observed for radicular cysts (p < .001, rs = -.471). Periapical granulomas are characterized by a M2-dominant macrophage polarization, while radicular cysts have significantly higher M1 macrophages. The higher degree of M1 macrophage polarization was significantly correlated with larger volume and higher CBCTPAVI scores of overall periapical lesion and periapical granuloma.

The article explores the rapidly growing role of artificial intelligence (AI) in reshaping legal practice, analyzing its potential applications, benefits, and the challenges that come with integrating AI into the legal field. As AI technology advances, it offers unprecedented opportunities to streamline legal processes, enhance efficiency, and empower legal professionals with powerful new tools. AI's capabilities in areas such as contract analysis, legal research, document review, and predictive analytics have the potential to significantly transform how legal work is conducted. The article examines the current state of AI adoption within legal practice and discusses its role in automating routine tasks, thus allowing lawyers to focus on more complex, value-added aspects of their work. However, the integration of AI into legal practice also brings about several challenges. Ethical concerns are paramount, particularly in areas such as algorithmic bias, transparency, and accountability. AI systems, which are often considered "black boxes," can make decisions that lack explainability, raising concerns about fairness and justice in legal proceedings. The article explores how AI may impact decision-making processes, especially in areas where human judgment has traditionally been paramount, such as in sentencing or dispute resolution. Furthermore, data privacy and the security of sensitive legal information are significant issues that need to be addressed when utilizing AI in legal practice. In addition to ethical and legal concerns, the article discusses the regulatory landscape surrounding AI in legal contexts. Various jurisdictions are beginning to consider how best to regulate AI's use in legal practice, with some adopting frameworks to ensure AI applications meet ethical and legal standards. The author explores ongoing efforts in Europe, the United States, and other regions to develop policies that address AI's implications in the legal sector. Another key point discussed is AI's potential to increase access to justice. By automating tasks, reducing costs, and improving the availability of legal services, AI could help bridge the gap in access to legal resources, particularly for individuals and businesses with limited financial means. AI systems could democratize legal knowledge and provide cost-effective legal advice, making legal services more accessible to a wider population.

The integration of artificial intelligence (AI) in healthcare presents a dual challenge: maximizing the efficiency of medical processes while safeguarding patient privacy. This comprehensive review examines the delicate balance between leveraging AI's potential in healthcare and preserving individual data privacy. Through analysis of recent literature, case studies, and regulatory frameworks, we explore the current landscape of AI applications in healthcare, associated privacy risks, and emerging solutions. Findings reveal that while AI significantly enhances diagnostic accuracy and treatment planning, it also raises concerns about data security and patient confidentiality. Key challenges include ensuring GDPR and HIPAA compliance, managing large-scale health data, and maintaining transparency in AI decision-making processes. Promising approaches such as federated learning and differential privacy emerge as potential solutions. This review underscores the need for a multidisciplinary approach involving healthcare providers, AI developers, ethicists, and policymakers to create robust, privacy-preserving AI systems in healthcare.

Study question What are the key considerations, validation frameworks, and safety guidelines required for the responsible implementation of Artificial Intelligence (AI) systems in MAR clinics? Summary answer The Croatia Consensus establishes internationally agreed-upon best practices for AI validation in MAR, ensuring patient safety, clinical excellence, regulatory compliance, and ethical implementation. What is known already AI applications are increasingly integrated into ART to optimise embryo selection, standardise clinical decision-making, and reduce variability. However, absence of internationally accepted validation frameworks, regulatory guidelines, and ethical oversight poses risks to patient safety and clinical efficacy. Current AI models often lack transparency, generalisation, and robust external validation. Bias in training datasets can lead to inequitable clinical outcomes. The need for structured AI governance in ART is pressing. The Croatia Consensus, formed by global experts (AI Fertility Society), aims to define best practices for AI validation and deployment in MAR clinics. Study design, size, duration A structured Delphi process involving 148 AI and MAR experts was conducted in 2024 to develop international guidelines for AI validation in ART. The consensus methodology included systematic literature reviews, expert panel discussions, and iterative feedback rounds. Topics covered included AI safety, validation protocols, data standardisation, regulatory compliance, and bias mitigation. The final consensus document was reviewed at the AI Fertility Society Meeting and endorsed by multidisciplinary stakeholders, including clinicians, embryologists, ethicists, and AI developers. Participants/materials, setting, methods Consensus guidelines were developed through contributions from embryologists, reproductive specialists, AI researchers, and regulatory experts. The process included a systematic review of AI applications in MAR, gap analysis of existing validation frameworks, and expert recommendations on AI validation strategies. Key aspects included standardised AI reporting (TRIPOD+AI compliance), real-world clinical validation across multiple centres, ethical risk mitigation, and transparent AI decision-making. AI system performance benchmarks were established using clinical outcome measures and patient safety indicators. Main results and the role of chance The Croatia Consensus establishes a comprehensive framework for AI validation in MAR, ensuring patient safety, regulatory compliance, and clinical efficacy. Key recommendations include multi-centre external validation of AI models to ensure generalisation across diverse patient populations, with the TRIPOD+AI framework recommended for transparent reporting. To mitigate bias, AI systems must undergo demographic audits, particularly in embryo selection, to prevent inequitable outcomes. Regulatory compliance with GDPR (EU), FDA (USA), and MHRA (UK) is required before clinical implementation. Transparency is critical; AI models must provide interpretable decisions, including confidence scores, feature importance, and performance metrics. Continuous post-implementation monitoring is essential to detect model drift and ensure patient safety over time. The consensus highlights that unvalidated AI models currently used in MAR clinics may introduce risks to patient outcomes. Implementing the Croatia Consensus framework will help standardise AI validation, mitigate risks, and ensure AI adoption in MAR is both evidence-based and clinically safe. Limitations, reasons for caution The consensus is based on expert opinions and current scientific literature; further empirical studies are required to validate AI best practices. The framework must evolve as AI capabilities and regulatory landscapes develop. Future research should focus on real-world AI deployment outcomes, patient safety, and long-term MAR success rates. Wider implications of the findings This is the first international AI validation framework in MAR. Standardising AI best practices will improve patient safety, optimise clinical outcomes, and enhance trust in AI-assisted fertility treatments. The framework provides a blueprint for MAR clinics, regulatory bodies, and AI developers, ensuring responsible AI integration into reproductive medicine. Trial registration number No

Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.