Artificial intelligence: expert systems.
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Artificial Intelligence↗
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Computer memory banks: promise of artificial intelligence.
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Artificial Intelligence↗
Artificial intelligence techniques for bioinformatics.
This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.
Algorithms↗
Artificial Intelligence Cannot Replace Peer Reviewers but May Help Editors Triage: A Comparative Analysis of a Large Language Model and Human Reviewer Recommendations at the American Journal of Sports Medicine.
BACKGROUND: The peer review system faces increasing strain from rising manuscript volumes, reviewer fatigue, and well-documented interreviewer disagreement. Large language models (LLMs) have shown potential to support the peer review process, but their ability to replicate editorial decisions at high-impact medical journals and their utility as manuscript screening tools remain unknown. PURPOSE: To compare the agreement between an LLM and the final editorial decision on manuscripts submitted to the American Journal of Sports Medicine and to evaluate the potential of LLMs as a manuscript screening tool. STUDY DESIGN: Cross-sectional agreement study. METHODS: Fifty-four manuscripts randomly selected from submissions to the American Journal of Sports Medicine (September 2024-October 2024) were reviewed by a locally deployed LLM (Ministral 3 14B; Mistral AI) using a standardized prompt. The artificial intelligence (AI) produced a categorical recommendation (reject, cascade, revision, or accept) and a numerical score (0-100) for each manuscript. Agreement with the final editorial decision was assessed by Cohen kappa (4-category model) for pooled human reviewers (n = 139 reviews) and the AI (n = 54). Screening performance was evaluated by positive predictive value (PPV), sensitivity, and specificity. RESULTS: Pooled human reviewers demonstrated fair agreement with the final decision (κ = 0.181 [P < .001]; 42.4% agreement), while the AI demonstrated slight, nonsignificant agreement (κ = 0.126 [P = .099]; 37.0% agreement). The AI recommended revision for 61.1% of manuscripts, of which 72.7% were ultimately rejected or cascaded, demonstrating systematic "revision bias." When the AI recommended rejection, 54.5% of those manuscripts were ultimately rejected and 27.3% were cascaded; when the AI recommended cascade, 50% were rejected and 50% were cascaded. However, when the AI recommended rejection or cascade (n = 21), 90.5% received a final decision of rejection or cascade (PPV, 90.5%; specificity, 81.8%). Manuscripts with an AI score <70 were rejected or cascaded 88.0% of the time (PPV, 88.0%). CONCLUSION: AI cannot replicate the nuanced judgment of human peer reviewers at a high-impact sports medicine journal. When AI recommended rejection or cascade, 90.5% of manuscripts received that final decision (descriptive PPV, 90.5%; 95% CI, 71.1%-97.3%), suggesting potential utility as an exploratory first-pass screening tool warranting further validation in larger cohorts. However, AI could not reliably distinguish manuscripts destined for outright rejection from those that would be cascaded to a sister journal-an important limitation for editorial triage applications.
Sports Medicine↗
The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.
BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.
Humans↗
Artificial intelligence in the diagnosis of low back pain.
Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.
Artificial Intelligence↗
Artificial intelligence in kidney cancer: a review of clinical applications across the disease spectrum.
PURPOSE OF REVIEW: This review examines recent advances (2024-2025) in the application of artificial intelligence (AI) to kidney cancer diagnosis, prognosis, and treatment planning. It categorizes studies across 13 clinical scenarios to assess where AI offers the most clinical utility. RECENT FINDINGS: AI models have demonstrated strong performance in a range of tasks including tumor grading, subtype classification, survival prediction, and risk stratification. Integration of radiomics, genomics, and histopathology has enabled personalized, noninvasive, and timely decision-making. The highest-performing models used CT-based radiomics, particularly for predicting progression-free and recurrence-free survival. However, performance varies across tasks and tumor subtypes, with lower accuracy in detecting oncocytomas or benign vs. malignant differentiation. AI applications in metastatic and nonresected cases remain underexplored, and ultrasound remains a largely under researched modality. While some models improve diagnostic accuracy and workflow efficiency, broader validation across diverse populations is still needed. SUMMARY: AI is transforming kidney cancer care across multiple clinical stages. Although promising, real-world implementation demands ongoing validation and postdeployment monitoring to prevent performance degradation due to distributional drift. AI's integration with multimodal data offers substantial potential to improve outcomes and reduce overtreatment.
Humans↗
Cardiac risk stratification in renal transplantation using a form of artificial intelligence.
The purpose of this study was to determine if an expert network, a form of artificial intelligence, could effectively stratify cardiac risk in candidates for renal transplant. Input into the expert network consisted of clinical risk factors and thallium-201 stress test data. Clinical risk factor screening alone identified 95 of 189 patients as high risk. These 95 patients underwent thallium-201 stress testing, and 53 had either reversible or fixed defects. The other 42 patients were classified as low risk. This algorithm made up the "expert system," and during the 4-year follow-up period had a sensitivity of 82%, specificity of 77%, and accuracy of 78%. An artificial neural network was added to the expert system, creating an expert network. Input into the neural network consisted of both clinical variables and thallium-201 stress test data. There were 5 hidden nodes and the output (end point) was cardiac death. The expert network increased the specificity of the expert system alone from 77% to 90% (p < 0.001), the accuracy from 78% to 89% (p < 0.005), and maintained the overall sensitivity at 88%. An expert network based on clinical risk factor screening and thallium-201 stress testing had an accuracy of 89% in predicting the 4-year cardiac mortality among 189 renal transplant candidates.
Adult↗
Patient positioning using artificial intelligence neural networks, trained magnetic field sensors and magnetic implants.
The purpose of this study was to evaluate the precision of a sensor and to ascertain the maximum distance between the sensor and the magnet, in a magnetic positioning system for external beam radiotherapy using a trained artificial intelligence neural network for position determination. Magnetic positioning for radiotherapy, previously described by Lennernäs and Nilsson, is a functional technique, but it is time consuming. The sensors are large and the distance between the sensor and the magnetic implant is limited to short distances. This paper presents a new technique for positioning, using an artificial intelligence neural network, which was trained to position the magnetic implant with at least 0.5 mm resolution in X and Y dimensions. The possibility of using the system for determination in the Z dimension, that is the distance between the magnet and the sensor, was also investigated. After training, this system positioned the magnet with a mean error of maximum 0.15 mm in all dimensions and up to 13 mm from the sensor. Of 400 test positions, 8 determinations had an error larger than 0.5 mm, maximum 0.55 mm. A position was determined in approximately 0.01 s.
Humans↗
Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know.
This review focuses on foundational knowledge about artificial intelligence (AI) in hepatology, exploring how AI, including machine learning and deep learning, leverages large-scale clinical data to transform the diagnosis, risk assessment, prognostication, and management of liver diseases. Online resources are described to offer fundamental AI knowledge and essential technical skills and to facilitate clinician participation across the entire AI lifecycle, ensuring they contribute not only as end users but also in development and deployment. Unlike traditional statistical approaches that prioritize interpretable parameters and clinical insight, AI focuses on maximizing predictive accuracy by identifying complex, often non-linear patterns using high-dimensional data, albeit often at the cost of model interpretability. AI is demonstrating clinical utility in liver histopathology and radiological imaging, significantly improving detection accuracy for cirrhosis, clinically significant portal hypertension, and hepatocellular carcinoma. Beyond diagnostics, AI-driven prediction models are emerging to provide personalized risk stratification for the development of liver-related complications and treatment guidance, based on complex data including longitudinal laboratory results, comorbidities, and co-medication use to monitor disease progression and therapy response. The field is rapidly expanding into novel areas such as analyzing patient-reported outcomes, genomic data, and real-time liver function monitoring, offering deeper mechanistic insights alongside clinical tools. Despite the potential to revolutionize hepatology practice and research, successful integration into routine care faces challenges. These include seamless workflow integration with existing electronic health records, establishing clear liability frameworks, and guaranteeing protection of patient privacy. Addressing these hurdles requires collaborative efforts from clinicians, researchers, and regulators to develop best practices and governance. Understanding the transformative capabilities, current applications, emerging frontiers, and essential implementation considerations is crucial for clinicians navigating the evolving AI landscape and responsibly utilizing its power for improved patient outcomes.
PROBAST+AI↗
Artificial intelligence techniques for the control of cancer cells.
NEWCHEM, an artificial intelligence system for the control of cancer cell growth, is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to develop optimal strategies for the selective control of cancer cell through qualitative reasoning from first principles at cellular level.
Computer Simulation↗
Use of conditional rule structure to automate clinical decision support: a comparison of artificial intelligence and deterministic programming techniques.
A rule-based computer system was developed to perform clinical decision-making support within a medical information system, oncology practice, and clinical research. This rule-based system, which has been programmed using deterministic rules, possesses features of generalizability, modularity of structure, convenience in rule acquisition, explanability, and utility for patient care and teaching, features which have been identified as advantages of artificial intelligence (AI) rule-based systems. Formal rules are primarily represented as conditional statements; common conditions and actions are stored in system dictionaries so that they can be recalled at any time to form new decision rules. Important similarities and differences exist in the structure of this system and clinical computer systems utilizing artificial intelligence (AI) production rule techniques. The non-AI rule-based system possesses advantages in cost and ease of implementation. The degree to which significant medical decision problems can be solved by this technique remains uncertain as does whether the more complex AI methodologies will be required.
Computers↗
The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.
Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.
Humans↗
Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.
Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearable health technologies. Leveraging machine learning and deep learning, AI can analyze complex data sets, including electronic health records, medical imaging, and genomic profiles, to identify patterns, predict disease progression, and recommend optimized treatment strategies. AI also has the potential to promote equity by enabling cost-effective, resource-efficient solutions in low-resource and remote settings, such as mobile diagnostics, wearable biosensors, and lightweight algorithms. Successful deployment requires addressing critical challenges, including data privacy, algorithmic bias, model interpretability, regulatory oversight, and maintaining human clinical oversight. Emphasizing scalable, ethical, and evidence-driven implementation, key strategies include clinician training in AI literacy, adoption of resource efficient tools, global collaboration, and robust regulatory frameworks to ensure transparency, safety, and accountability. By complementing rather than replacing healthcare professionals, AI can reduce errors, optimize resources, improve patient outcomes, and expand access to quality care. This review emphasizes the responsible integration of AI as a powerful catalyst for innovation, sustainability, and equity in healthcare delivery worldwide.
Humans↗
The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.
INTRODUCTION: Intimate partner violence (IPV), encompassing physical, sexual, emotional and economic abuse, remains a pervasive global health concern. Traditional prevention efforts face obstacles such as underreporting, delayed detection and limited personalised support. Emerging artificial intelligence (AI) approaches offer new opportunities to enhance IPV prevention. AIM: This systematic review maps and synthesises evidence on AI-driven tools in IPV prevention based on studies published between 2004 and 2024. METHODS: Following PRISMA 2020 guidelines and PROSPERO registration, we searched PubMed, Embase, CINAHL, PsycINFO, IEEE Xplore and Web of Science. Eligible studies explicitly evaluated AI technologies targeting IPV prediction, screening, intervention or support delivery. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Of 1304 records initially identified, 41 studies met eligibility criteria. AI applications ranged from machine learning (ML) for risk prediction and natural language processing (NLP) for IPV detection in clinical and social media data, to image analysis for forensic evaluation and chatbot-based support. Predictive modelling demonstrated strong discriminative performance, while NLP-based screening detected IPV with notable sensitivity. Chatbots showed feasibility and user acceptability, but evidence of their direct impact on reducing IPV incidence was limited, with one randomised controlled trial showing a modest reduction. Key challenges identified included algorithmic bias, data privacy risks and barriers to integration across health and social care systems. DISCUSSION: AI-informed interventions show promise for improving IPV detection, risk assessment, and scalable support, but questions remain about long-term effectiveness, ethical fairness, transparency and equitable implementation. Future interdisciplinary research should address these concerns to responsibly deploy AI in IPV prevention. RELEVANCE TO CLINICAL PRACTICE: The findings highlight the importance of trauma-informed, culturally responsive care and provider training in AI applications. Nurse-led innovation and policy advocacy will be crucial for safe, equitable integration of AI in IPV prevention.
Artificial Intelligence↗
The use of artificial intelligence technology to predict lymph node spread in men with clinically localized prostate carcinoma.
BACKGROUND: The current study assesses artificial intelligence methods to identify prostate carcinoma patients at low risk for lymph node spread. If patients can be assigned accurately to a low risk group, unnecessary lymph node dissections can be avoided, thereby reducing morbidity and costs. METHODS: A rule-derivation technology for simple decision-tree analysis was trained and validated using patient data from a large database (4,133 patients) to derive low risk cutoff values for Gleason sum and prostate specific antigen (PSA) level. An empiric analysis was used to derive a low risk cutoff value for clinical TNM stage. These cutoff values then were applied to 2 additional, smaller databases (227 and 330 patients, respectively) from separate institutions. RESULTS: The decision-tree protocol derived cutoff values of < or = 6 for Gleason sum and < or = 10.6 ng/mL for PSA. The empiric analysis yielded a clinical TNM stage low risk cutoff value of < or = T2a. When these cutoff values were applied to the larger database, 44% of patients were classified as being at low risk for lymph node metastases (0.8% false-negative rate). When the same cutoff values were applied to the smaller databases, between 11 and 43% of patients were classified as low risk with a false-negative rate of between 0.0 and 0.7%. CONCLUSIONS: The results of the current study indicate that a population of prostate carcinoma patients at low risk for lymph node metastases can be identified accurately using a simple decision algorithm that considers preoperative PSA, Gleason sum, and clinical TNM stage. The risk of lymph node metastases in these patients is < or = 1%; therefore, pelvic lymph node dissection may be avoided safely. The implications of these findings in surgical and nonsurgical treatment are significant.
Acid Phosphatase↗
Applications of artificial intelligence systems in the analysis of epidemiological data.
A brief review of the germane literature suggests that the use of artificial intelligence (AI) statistical algorithms in epidemiology has been limited. We discuss the advantages and disadvantages of using AI systems in large-scale sets of epidemiological data to extract inherent, formerly unidentified, and potentially valuable patterns that human-driven deductive models may miss.
Algorithms↗
Digital pathology, image analysis, and artificial intelligence in liver disease.
Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and in the assessment and management of transplantation. Although quantitative image analysis techniques have been applied to liver disease in research settings for over 50 years, recent improvements in image resolution, data storage, and the availability of advanced AI methods such as deep learning have driven multiple exciting developments. In this Review, we summarise the advancements in digital pathology, image analysis, and AI in liver disease. Key challenges such as access to and the logistics of using digital solutions, quality issues, and appropriate guidance in research and clinical use are reviewed, along with potential solutions to these challenges in the context of liver pathology and liver disease. Digital technologies are well established in liver pathology research, and access in clinical practice is increasing, with potential to address current laboratory challenges. Further evaluation is required to assess real-world effectiveness, clinical safety, and implementation of AI tools in liver pathology.
Journal Article↗