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Results for “Clinical decision support”

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At least 19 recordsLinked to original sources
PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗
PubMed2026

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease↗
PubMedDate not supplied

The evaluation of clinical decision support systems: what is necessary versus what is interesting.

The evaluation of clinical decision support systems has long been recognized as an important part of the overall field of medical computing, as well as a complex and varied field in its own right. There are a number of purposes for which such an evaluation might be performed. This paper focuses on one of those purposes, the assurance of safety when preparing to release the system for general use. The assurance of safety involves two major stages: (1) a comprehensive validation of the system's knowledge and advice, and (2) a period of use which exposes the system's advice and interface to the clinical environment.

Decision Making, Computer-Assisted↗
PubMed2026

An AI-assisted Clinical Decision Support System for Green Classification of Cystocele on Dynamic Transperineal Ultrasound.

Green classification of cystocele on dynamic transperineal ultrasound (TPUS) remains operator-dependent because it requires manual frame selection and landmark-based assessment of the Valsalva maneuver. We developed a workflow-oriented AI-assisted clinical decision support system for automated urethrovesical junction localization and dynamic Green classification and prospectively evaluated its standalone and reader-support performance. This diagnostic accuracy and reader study included 881 patients from a tertiary referral hospital, comprising a retrospective development cohort (n = 688) and an independent prospective test cohort (n = 193). A nested subset of 67 prospective patients was used for a reader study involving two junior and two intermediate radiologists under unaided and AI-assisted conditions. In the complete prospective test cohort, Green-AttGRU achieved a macro-averaged AUC of 0.939 (95% CI, 0.897-0.971) and an overall accuracy of 0.902 (95% CI, 0.860-0.943). In the reader study, overall accuracy increased from 0.761 to 0.821 without AI to 0.851-0.881 with AI, while macro-F1 increased from 0.660 to 0.777 to 0.820-0.860. Overall inter-reader agreement increased from a Fleiss' κ of 0.453 to 0.786, and pooled median interpretation time decreased from 26.7 s to 9.9 s. These findings support the preliminary feasibility of the system as a workflow-oriented decision-support tool for dynamic TPUS interpretation.

Humans↗
PubMed1992

A critical appraisal of the literature on the effects of computer-based clinical decision support systems on clinician performance and patient outcomes.

OBJECTIVE: To review the evaluations of computer-based clinical decision support systems (CDSS's). DATA SOURCES: The literature collected in the MEDLARS, EMBASE, SCISEARCH and INSPEC databases was searched from 1974 to the present. The reference lists of relevant articles were reviewed as were conference proceedings. STUDY SELECTION: Prospective, controlled studies were included. Studies were rated for methodological quality. DATA EXTRACTION: Study quality was assessed and data on study setting, subjects, method of allocation, and computer system were collected and verified using a structured form. CONCLUSIONS: There is considerable heterogeneity in both systems evaluated and design features of those systems. Future evaluations of CDSS's should focus on methodological issues in order to enhance overall quality of evaluations.

Clinical Medicine↗
PubMedDate not supplied

Systems to support clinical decisions: automated medical signal analysis.

The benefits and capabilities of an automated medical signal analysis system that can lead to more effective patient care are identified, the capabilities of different types of systems are briefly mentioned, and automated systems that support nominative and managerial decisions are described. The need for the practicing physician to anticipate computer hardware limitations and potential errors in programming are briefly discussed.

Decision Making↗
PubMedDate not supplied

Computer-based support for clinical decision making.

Although computers are now commonly used for financial purposes in hospitals and physicians' offices, most physicians do not routinely use them in patient care. And in hospitals where laboratory data are provided on computer terminals, the displays are often difficult to use and programs that offer assistance in interpreting the data are usually unavailable. We have developed decision support programs that are widely used with the clinical computing system at our hospital. This paper describes the programs and how the clinicians use them.

Boston↗
PubMed2025

Understanding recurrence in Mycobacterium avium complex pulmonary disease: genotypic strategies to support clinical decision-making.

Pulmonary disease caused by Mycobacterium avium complex (MAC-PD) is a chronic, recurrent disease, and its high recurrence rate after treatment makes clinical management difficult. Distinguishing whether recurrence is due to persistence of existing strains or reinfection with new strains is essential for establishing treatment strategies, preventing overuse of antimicrobials, and establishing infection control measures. According to reports, 54%-74% of MAC-PD recurrence is due to reinfection, which may be mainly related to environmental reservoirs such as household water supply. In this review, we present various clinical scenarios in which MAC-PD recurrence may occur and examine genotyping techniques as a strategy to distinguish and respond to them. From traditional methods such as IS1245-based restriction fragment length polymorphism, pulsed-field gel electrophoresis, and hsp65 and rpoB gene sequencing to high-resolution analysis techniques such as multilocus sequence testing and whole-genome sequencing, the latest molecular typing methods are comprehensively summarized. Integrating these genotype data into clinical settings, standardizing single-nucleotide polymorphism-based interpretation thresholds, and promoting the establishment of a global MAC strain database will make a substantial contribution to more accurately distinguishing the recurrence mechanisms of MAC-PD and establishing personalized treatment strategies.IMPORTANCEThe global burden of nontuberculous mycobacterial pulmonary disease (PD) is increasing, with Mycobacterium avium (MAC)-PD being the most prevalent and clinically challenging form. Its low treatment success rates, high frequency of recurrence, and persistent environmental exposure complicate both diagnosis and management. A critical clinical issue is determining whether recurrence represents true relapse, due to persistence of the original strain, or reinfection with a new strain, as this guides treatment and prevents overtreatment. Genotypic strategies capable of resolving strain-level differences can improve diagnostic accuracy, prevent misclassification, and ultimately support more informed treatment decisions. Therefore, integrating genotyping data into clinical workflows, standardizing single-nucleotide polymorphism thresholds, and establishing a global MAC strain database will not only support personalized treatment but also enhance the broader public health response to this disease.

Humans↗
PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗
PubMed1992

An extended SQL for temporal data management in clinical decision-support systems.

We are developing a database implementation to support temporal data management for the T-HELPER physician workstation, an advice system for protocol-based care of patients who have HIV disease. To understand the requirements for the temporal database, we have analyzed the types of temporal predicates found in clinical-trial protocols. We extend the standard relational data model in three ways to support these querying requirements. First, we incorporate timestamps into the two-dimensional relational table to store the temporal dimension of both instant- and interval-based data. Second, we develop a set of operations on timepoints and intervals to manipulate timestamped data. Third, we modify the relational query language SQL so that its underlying algebra supports the specified operations on timestamps in relational tables. We show that our temporal extension to SQL meets the temporal data-management needs of protocol-directed decision support.

Acquired Immunodeficiency Syndrome↗
PubMed1992

DecisionNET: database/network support for clinical decision analysts.

DecisionNET is a proposed remote database/wide-area computer network that should cultivate quantitative approaches to medicine by providing rapid access to medical facts, literature citations, decision models, and commentary of specific use to those involved with quantitative clinical decision making. Increasing accessibility to data and providing a forum for exchange of ideas should prove to be a significant asset to clinicians, either as modelers or as clients. Further work will address specific database implementation, development of a query engine, and coordination of the communications infrastructure. DecisionNET provides a unique model for the growth and maintenance of other medical knowledge bases.

Archives↗
PubMed2026

Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disorder with heterogeneous presentations, making diagnosis and management highly dependent on imaging and clinical expertise. This narrative review examines how artificial intelligence (AI) and machine learning (ML) are transforming FMD care. AI-enhanced imaging, particularly convolutional neural network-based analysis, improves detection of the characteristic "string-of-beads" pattern on CT angiography, magnetic resonance angiography, and ultrasound, although FMD-specific validation remains limited. ML models facilitate risk stratification, prediction of disease progression, and early identification of complications such as aneurysms and stroke by integrating clinical, imaging, and genomic data. AI-driven clinical decision support systems further enable personalized treatment selection through pharmacogenomic insights and robot-assisted interventions. Despite promising real-world applications, challenges persist, including limited large-scale datasets, workflow integration, regulatory barriers, and algorithmic bias affecting underrepresented populations. Future advances in explainable AI, federated learning, and digital health integration may enable a shift toward predictive, patient-centered FMD management.

Humans↗
PubMed2026

Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends.

Antimicrobial resistance (AMR) constrains effective treatment and carries implications for infection control, surveillance, and public health. The World Health Organization (WHO) priority bacterial pathogen framework has intensified the need for diagnostic innovation by redefining research priorities around organisms combining high disease burden with complex resistance profiles. Molecular diagnostics have accordingly moved beyond culture-based workflows, integrating rapid pathogen identification, resistance-marker detection, genomic surveillance, and clinical decision support. The present study conducted a bibliometric mapping of the literature on WHO priority pathogens. Rather than addressing resistance at a general level or a single pathogen or technology, it integrates priority pathogens, molecular platforms, and resistance markers within a single framework, tracing their joint thematic and temporal evolution along an explicit pathogen-platform-marker axis. Scopus-indexed articles and reviews (2000-2025) were retrieved, yielding 1746 publications after screening adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Analyses used Bibliometrix/Biblioshiny, R, and VOSviewer. The literature expanded markedly after 2018, led by China and the United States. Methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis, Enterococcus faecium, and the Enterobacterales-carbapenemase axis constituted the principal thematic cores, whereas conventional polymerase chain reaction (PCR)/nucleic acid amplification testing (NAAT) and whole-genome sequencing were the dominant platforms. Overall, the field has evolved from pathogen detection into an AMR-centered translational domain encompassing resistance prediction, genomic epidemiology, surveillance, and clinical decision support. Diagnostic development, stewardship, and surveillance depend on hybrid workflows coupling rapid marker-targeted assays with genome-based characterization, delivering actionable resistance within clinically meaningful timeframes, and extending coverage to underrepresented pathogens and platforms.

Humans↗
PubMed1992

Experiences of developed management in the Cardiovascular Service at Royal Adelaide Hospital.

Devolved management structures are on trial at Royal Adelaide Hospital with assistance from the Clinical Devolution Project. The trial is to assess the suitability of devolved management structures to support clinical decision making, and to evaluate financial management reporting systems and information support systems. A nurse at Assistant Director of Nursing level and a doctor head each of the three trial Clinical services. Both have equal responsibilities and delegations of authority for day to day operations utilising an allocated budget. The impact of devolution of responsibility to clinical units is considerable. Such reorganisation presents opportunities and difficulties. Nurse/doctor collaboration is essential to manage high patient throughout effectively and efficiently as well as to prepare for the impact of casemix and output based funding. Clinical department size, budget allocation, roles and organisational infrastructure affect the success or failure of devolved management structures, even if a service works within an allocated budget and achieves high patent throughput and decreasing length of patient stay.

Cardiovascular Diseases↗