BMC Medical Informatics and Decision Making最新文献

{"title":"AI-Driven decision-making for personalized elderly care: a fuzzy MCDM-based framework for enhancing treatment recommendations.","authors":"Abeer Aljohani","doi":"10.1186/s12911-025-02953-5","DOIUrl":"10.1186/s12911-025-02953-5","url":null,"abstract":"<p><strong>Background: </strong>Global healthcare systems face enormous challenges due to the ageing population, demanding novel measures to assure long-term efficacy and viability. The expanding senior population, which requires specialised and efficient healthcare solutions, emphasises the importance of improving healthcare sustainability. Recognising the importance of personalised healthcare recommendations in improving patient outcomes as well as facility sustainability, this study tackles the crucial need for targeted treatments to help the elderly navigate the complicated healthcare landscape.</p><p><strong>Objectives: </strong>Through the integration of automation with the Fuzzy VIKOR approach as well as Electronic Health Record (EHR) data, this work seeks to create an automated decision-making mechanism that improves personalised healthcare suggestions for the elderly. By using automated data-driven observations, Fuzzy VIKOR to handle decision-making uncertainty as well as the clinical depth of EHR data, the primary objective is to increase the efficacy and accuracy of treatment choices. In order to guarantee that treatment recommendations are not only medically beneficial but also in line with each patient's needs and preferences, this research aims to close the gap between automated intelligence as well as patient-centered care.</p><p><strong>Method: </strong>The Fuzzy VIKOR approach is used with Electronic Health Record (EHR) data to establish a strong framework for personalised healthcare recommendations. AI techniques are employed to enhance data processing, while Fuzzy VIKOR is used to control uncertainty in decision-making, whereas EHR data gives comprehensive clinical insights. The combination of these aspects enables the creation of a system that compensates for uncertainties in medical knowledge and patient preferences, culminating in a ranked array of treatment alternatives customised to the difficulties of healthcare decision-making for the aged.</p><p><strong>Results: </strong>The study shows how the proposed methodology improves therapy selection for senior populations. By combining AI-powered analysis, Fuzzy VIKOR, and EHR data, the study provides a refined and personalised approach to healthcare recommendations, providing ranked treatment alternatives based on individual characteristics and preferences. The findings demonstrate the potential of this strategy to handle healthcare complexity and contribute to the developing era of precision medicine.</p><p><strong>Conclusion: </strong>Finally this study makes an important contribution to the continuing discussion about the sustainability of healthcare for the elderly. The combination of AI-driven methodologies, the Fuzzy VIKOR technique and EHR data offers a promising approach to improving therapy selection in the setting of precision medicine. By accepting personalised healthcare recommendations, this study anticipates a future in which elderly people's unique characteristi","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"119"},"PeriodicalIF":3.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.","authors":"Na Hyeon Yu, Daeun Shin, Ik Hee Ryu, Tae Keun Yoo, Kyungmin Koh","doi":"10.1186/s12911-025-02950-8","DOIUrl":"10.1186/s12911-025-02950-8","url":null,"abstract":"<p><strong>Background: </strong>Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease risk assessment. This study aimed to develop a machine learning model to predict RVO risk in the general population using such tabular health data, without requiring coding expertise or retinal imaging.</p><p><strong>Methods: </strong>We utilized data from the Korea National Health and Nutrition Examination Surveys (KNHANES) collected between 2017 and 2020 to develop the RVO prediction model, with external validation performed using independent data from KNHANES 2021. Model construction was conducted using Orange Data Mining, an open-source, code-free, component-based tool with a user-friendly interface, and Google Vertex AI. An easy-to-use oversampling function was employed to address class imbalance, enhancing the usability of the workflow. Various machine learning algorithms were trained by incorporating all features from the health check-up data in the development set. The primary outcome was the area under the receiver operating characteristic curve (AUC) for identifying RVO.</p><p><strong>Results: </strong>All machine learning training was completed without the need for coding experience. An artificial neural network (ANN) with a ReLU activation function, developed using Orange Data Mining, demonstrated superior performance, achieving an AUC of 0.856 (95% confidence interval [CI], 0.835-0.875) in internal validation and 0.784 (95% CI, 0.763-0.803) in external validation. The ANN outperformed logistic regression and Google Vertex AI models, though differences were not statistically significant in internal validation. In external validation, the ANN showed a marginally significant improvement over logistic regression (P = 0.044), with no significant difference compared to Google Vertex AI. Key predictive variables included age, household income, and blood pressure-related factors.</p><p><strong>Conclusion: </strong>This study demonstrates the feasibility of developing an accessible, cost-effective RVO risk prediction tool using health check-up data and no-code machine learning platforms. Such a tool has the potential to enhance early detection and preventive strategies in general healthcare settings, thereby improving patient outcomes.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"118"},"PeriodicalIF":3.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NLP modeling recommendations for restricted data availability in clinical settings.","authors":"Fabián Villena, Felipe Bravo-Marquez, Jocelyn Dunstan","doi":"10.1186/s12911-025-02948-2","DOIUrl":"10.1186/s12911-025-02948-2","url":null,"abstract":"<p><strong>Background: </strong>Clinical decision-making in healthcare often relies on unstructured text data, which can be challenging to analyze using traditional methods. Natural Language Processing (NLP) has emerged as a promising solution, but its application in clinical settings is hindered by restricted data availability and the need for domain-specific knowledge.</p><p><strong>Methods: </strong>We conducted an experimental analysis to evaluate the performance of various NLP modeling paradigms on multiple clinical NLP tasks in Spanish. These tasks included referral prioritization and referral specialty classification. We simulated three clinical settings with varying levels of data availability and evaluated the performance of four foundation models.</p><p><strong>Results: </strong>Clinical-specific pre-trained language models (PLMs) achieved the highest performance across tasks. For referral prioritization, Clinical PLMs attained an 88.85 % macro F1 score when fine-tuned. In referral specialty classification, the same models achieved a 53.79 % macro F1 score, surpassing domain-agnostic models. Continuing pre-training with environment-specific data improved model performance, but the gains were marginal compared to the computational resources required. Few-shot learning with large language models (LLMs) demonstrated lower performance but showed potential in data-scarce scenarios.</p><p><strong>Conclusions: </strong>Our study provides evidence-based recommendations for clinical NLP practitioners on selecting modeling paradigms based on data availability. We highlight the importance of considering data availability, task complexity, and institutional maturity when designing and training clinical NLP models. Our findings can inform the development of effective clinical NLP solutions in real-world settings.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"116"},"PeriodicalIF":3.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.","authors":"Bauke Arends, Melle Vessies, Dirk van Osch, Arco Teske, Pim van der Harst, René van Es, Bram van Es","doi":"10.1186/s12911-025-02897-w","DOIUrl":"10.1186/s12911-025-02897-w","url":null,"abstract":"<p><strong>Background: </strong>Clinical machine learning research and artificial intelligence driven clinical decision support models rely on clinically accurate labels. Manually extracting these labels with the help of clinical specialists is often time-consuming and expensive. This study tests the feasibility of automatic span- and document-level diagnosis extraction from unstructured Dutch echocardiogram reports.</p><p><strong>Methods: </strong>We included 115,692 unstructured echocardiogram reports from the University Medical Center Utrecht, a large university hospital in the Netherlands. A randomly selected subset was manually annotated for the occurrence and severity of eleven commonly described cardiac characteristics. We developed and tested several automatic labelling techniques at both span and document levels, using weighted and macro F1-score, precision, and recall for performance evaluation. We compared the performance of span labelling against document labelling methods, which included both direct document classifiers and indirect document classifiers that rely on span classification results.</p><p><strong>Results: </strong>The SpanCategorizer and MedRoBERTa.nl models outperformed all other span and document classifiers, respectively. The weighted F1-score varied between characteristics, ranging from 0.60 to 0.93 in SpanCategorizer and 0.96 to 0.98 in MedRoBERTa.nl. Direct document classification was superior to indirect document classification using span classifiers. SetFit achieved competitive document classification performance using only 10% of the training data. Utilizing a reduced label set yielded near-perfect document classification results.</p><p><strong>Conclusion: </strong>We recommend using our published SpanCategorizer and MedRoBERTa.nl models for span- and document-level diagnosis extraction from Dutch echocardiography reports. For settings with limited training data, SetFit may be a promising alternative for document classification. Future research should be aimed at training a RoBERTa based span classifier and applying English based models on translated echocardiogram reports.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"115"},"PeriodicalIF":3.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of large language model (LLM) evaluations in clinical medicine.","authors":"Sina Shool, Sara Adimi, Reza Saboori Amleshi, Ehsan Bitaraf, Reza Golpira, Mahmood Tara","doi":"10.1186/s12911-025-02954-4","DOIUrl":"10.1186/s12911-025-02954-4","url":null,"abstract":"<p><strong>Background: </strong>Large Language Models (LLMs), advanced AI tools based on transformer architectures, demonstrate significant potential in clinical medicine by enhancing decision support, diagnostics, and medical education. However, their integration into clinical workflows requires rigorous evaluation to ensure reliability, safety, and ethical alignment.</p><p><strong>Objective: </strong>This systematic review examines the evaluation parameters and methodologies applied to LLMs in clinical medicine, highlighting their capabilities, limitations, and application trends.</p><p><strong>Methods: </strong>A comprehensive review of the literature was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, and arXiv databases, encompassing both peer-reviewed and preprint studies. Studies were screened against predefined inclusion and exclusion criteria to identify original research evaluating LLM performance in medical contexts.</p><p><strong>Results: </strong>The results reveal a growing interest in leveraging LLM tools in clinical settings, with 761 studies meeting the inclusion criteria. While general-domain LLMs, particularly ChatGPT and GPT-4, dominated evaluations (93.55%), medical-domain LLMs accounted for only 6.45%. Accuracy emerged as the most commonly assessed parameter (21.78%). Despite these advancements, the evidence base highlights certain limitations and biases across the included studies, emphasizing the need for careful interpretation and robust evaluation frameworks.</p><p><strong>Conclusions: </strong>The exponential growth in LLM research underscores their transformative potential in healthcare. However, addressing challenges such as ethical risks, evaluation variability, and underrepresentation of critical specialties will be essential. Future efforts should prioritize standardized frameworks to ensure safe, effective, and equitable LLM integration in clinical practice.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"117"},"PeriodicalIF":3.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circulating CCN6/WISP3 in type 2 diabetes mellitus patients and its correlation with insulin resistance and inflammation: statistical and machine learning analyses.","authors":"Reza Afrisham, Yasaman Jadidi, Nariman Moradi, Seyed Mohammad Ayyoubzadeh, Reza Fadaei, Omid Kiani Ghalesardi, Vida Farrokhi, Shaban Alizadeh","doi":"10.1186/s12911-025-02957-1","DOIUrl":"10.1186/s12911-025-02957-1","url":null,"abstract":"<p><strong>Introduction: </strong>Cellular Communication Network Factor 6 (CCN6) is an adipokine whose production undergoes significant alterations in metabolic disorders. Given the well-established link between obesity-induced adipokine dysfunction and the development of insulin resistance and type 2 diabetes mellitus (T2DM), this study investigates the potential role of CCN6 as a biomarker for T2DM. The present study aimed to investigate the association between serum CCN6 levels and T2DM, as well as its risk factors, for the first time.</p><p><strong>Methods: </strong>In this case-control study, a total of 80 individuals diagnosed with T2DM and 80 healthy control individuals, who referred to Shariati hospital (Tehran, Iran), were included in the study. Biochemical parameters including fasting blood glucose (FBG), aspartate transaminase (AST), alanine transaminase (ALT), triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were determined using the AutoAnalyzer instrument. The circulating levels of CCN6, adiponectin, Tumor necrosis factor-α (TNF)-α, Interleukin 6 (IL-6), and insulin were quantified using ELISA. The Student t-test was applied to data that presented as mean ± standard deviations (SD). Moreover, the Gini Index was utilized to determine the weight of each factor in T2DM classification. Additionally, various machine learning models were employed to develop classifiers for predicting T2DM.</p><p><strong>Results: </strong>T2DM patients demonstrated significantly lower levels of CCN6 (1259.76 ± 395.02 pg/ml) compared to controls (1979.17 ± 471.99 pg/ml, P < 0.001), as well as lower levels of adiponectin (P < 0.001) and higher levels of TNF-α and IL-6 (P < 0.001) compared to non-T2DM individuals. In the T2DM group, CCN6 exhibited negative correlations with insulin, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), body mass index (BMI), IL-6, and TNF-α. Logistic regression analysis indicated an increased risk of T2DM, with a CCN6 cutoff value of 1527.95 pg/mL distinguishing T2DM patients with 86.3% sensitivity and 73.8% specificity. The Gini Index highlighted that HOMA-IR, IL6, and CCN6 had the highest weighting on T2DM.</p><p><strong>Conclusion: </strong>Our research identified a significant and negative association between serum CCN6 levels and the likelihood of T2DM, as well as inflammation biomarkers (IL-6 and TNF-α). CCN6 shows promise as a potential biomarker for T2DM; however, further investigations are necessary to validate this finding and assess its clinical utility.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"114"},"PeriodicalIF":3.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based classification of dementia using image representation of subcortical signals.","authors":"Shivani Ranjan, Ayush Tripathi, Harshal Shende, Robin Badal, Amit Kumar, Pramod Yadav, Deepak Joshi, Lalan Kumar","doi":"10.1186/s12911-025-02924-w","DOIUrl":"10.1186/s12911-025-02924-w","url":null,"abstract":"<p><strong>Background: </strong>Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementia cases (AD and FTD) is crucial for effective medical care, as both conditions have similar early-symptoms. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI).</p><p><strong>Methods: </strong>This study aims to develop a deep learning-based classification system for dementia by analyzing EEG derived scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. Scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique are utilized. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to validate the efficacy of the proposed method: the online BrainLat dataset (128 channels, comprising 16 AD, 13 FTD, and 19 healthy controls (HC)) and the in-house IITD-AIIA dataset (64 channels, including subjects with 10 AD, 9 MCI, and 8 HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes in both data sets.</p><p><strong>Results: </strong>The best results were achieved using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yield accuracies of 94.17 <math><mo>%</mo></math> and 77.72 <math><mo>%</mo></math> on the BrainLat and IITD-AIIA datasets, respectively.</p><p><strong>Conclusions: </strong>The results highlight that the image representation-based deep learning approach has the potential to differentiate various stages of dementia. It pave the way for more accurate and early diagnosis, which is crucial for the effective treatment and management of debilitating conditions.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"113"},"PeriodicalIF":3.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Offline visit intention of online patients: the Grice's maxims and patient involvement.","authors":"Xianye Cao, Yongmei Liu, Zian Fang, Zhangxiang Zhu","doi":"10.1186/s12911-025-02861-8","DOIUrl":"10.1186/s12911-025-02861-8","url":null,"abstract":"<p><p>Online Healthcare Consulting Services (OHCS) can benefit physicians and patients. However, it is unclear how OHCS and what types of persuasive content enhance patients' intentions to visit offline. Based on the Elaboration Likelihood Model (ELM) and Grice's maxims of the Cooperative Principle, we formulated hypotheses related to factors in the central route, peripheral route, and patient involvement that influence patients' offline visit intentions. We used the amount of information, reliability, relevance, and understandability to measure information quality. By collecting data from an online healthcare site, we employed a regression model to evaluate our hypotheses. The results revealed that central route factors (amount of information, reliability, relevance, and understandability) and peripheral cues positively affect patients' offline visits. We also verified that patient involvement increases the impact of central route factors. This study extended the application of ELM and Grice's maxims in the field of OHCS, offering insights into how patients form intentions to visit offline through persuasive online content and providing valuable practical guidance for online physicians.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"112"},"PeriodicalIF":3.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.","authors":"Justin Blackman, Richard Veerapen","doi":"10.1186/s12911-025-02891-2","DOIUrl":"10.1186/s12911-025-02891-2","url":null,"abstract":"<p><p>The necessity for explainability of artificial intelligence technologies in medical applications has been widely discussed and heavily debated within the literature. This paper comprises a systematized review of the arguments supporting and opposing this purported necessity. Both sides of the debate within the literature are quoted to synthesize discourse on common recurring themes and subsequently critically analyze and respond to it. While the use of autonomous black box algorithms is compellingly discouraged, the same cannot be said for the whole of medical artificial intelligence technologies that lack explainability. We contribute novel comparisons of unexplainable clinical artificial intelligence tools, diagnosis of idiopathy, and diagnoses by exclusion, to analyze implications on patient autonomy and informed consent. Applying a novel approach using comparisons with clinical practice guidelines, we contest the claim that lack of explainability compromises clinician due diligence and undermines epistemological responsibility. We find it problematic that many arguments in favour of the practical, ethical, or legal necessity of clinical artificial intelligence explainability conflate the use of unexplainable AI with automated decision making, or equate the use of clinical artificial intelligence with the exclusive use of clinical artificial intelligence.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"111"},"PeriodicalIF":3.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11881432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.","authors":"Razan Alkhanbouli, Hour Matar Abdulla Almadhaani, Farah Alhosani, Mecit Can Emre Simsekler","doi":"10.1186/s12911-025-02944-6","DOIUrl":"10.1186/s12911-025-02944-6","url":null,"abstract":"<p><p>Explainable Artificial Intelligence (XAI) enhances transparency and interpretability in AI models, which is crucial for trust and accountability in healthcare. A potential application of XAI is disease prediction using various data modalities. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol, synthesizing findings from 30 selected studies to examine XAI's evolving role in disease prediction. It explores commonly used XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), and their impact across medical fields in disease prediction. The review highlights key gaps, including limited dataset diversity, model complexity, and reliance on single data types, emphasizing the need for greater interpretability and data integration. Addressing these issues is crucial for advancing AI in healthcare. This study contributes by outlining current challenges and potential solutions, suggesting directions for future research to develop more reliable and robust XAI methods.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"110"},"PeriodicalIF":3.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}