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PubMed2026

Artificial Intelligence Technologies in Nursing Clinical Decision-Making: An Umbrella Review.

AIM: To describe contemporary peer-reviewed literature on artificial intelligence in nurses' clinical decision-making. METHODS: An umbrella review of literature reviews. DATA SOURCES: Four major databases were searched for reviews published between 2019 and 2024. RESULTS: Sixteen literature reviews reported on 965 nursing artificial intelligence primary studies. The studies focused on technology development and emerging performance evaluations, whilst real-world testing or implementation in nursing clinical settings was rare. Rigorous comparative analyses were lacking. While artificial intelligence demonstrates promise in decision-making, challenges such as a lack of controlled studies, algorithmic bias, limited reproducibility and insufficient clinical trials hinder its practical impact. Ethical concerns, transparency and patient data privacy issues pose barriers to AI integration in nursing practice. Ethical and legal guidelines for patient privacy are needed and should be taught along with AI literacy training for nurses. CONCLUSIONS: Artificial intelligence has the potential to enhance clinical nursing decision-making, although evidence is limited by too few examples of nurse participation during development. Underutilisation in administrative nursing functions hinders implementation. Nurses should assume a central role in the design and development of AI applications to ensure that these technologies address the realities of nursing practice. With such improvements, artificial intelligence can transform nursing practice, improve nurses' clinical decision-making and ultimately enhance consumer healthcare outcomes. PATIENT OR PUBLIC INVOLVEMENT: No Patient or Public Involvement. REPORTING METHOD: While there is no reporting checklist for umbrella reviews, the PRISMA guide for systematic reviews was followed.

Artificial Intelligence↗
PubMed2006

Postsurgical outcome in pediatric patients with epilepsy: a comparison of patients with intellectual disabilities, subaverage intelligence, and average-range intelligence.

PURPOSE: Intellectual disabilities are often associated with bilateral or diffuse morphologic brain damage. The chances of becoming seizure free after focal surgery are therefore considered to be worse in patients with intellectual disabilities. The risk of postoperative cognitive deficits could increase because diffuse brain damage lowers the patient's ability to compensate for surgically induced deficits. Several studies in adult patients have indicated that IQ alone is not a good predictor of postoperative cognitive and seizure outcome. Our study evaluated this subject in children and adolescents. METHODS: Pediatric patients with intellectual disabilities (IQ < or = 70), subaverage intelligence (IQ between 71 and 85), or average-range intelligence (IQ > 85) were matched according to several clinical and etiologic criteria to determine the influence of IQ (N = 66). RESULTS: No dependency of seizure outcome, postoperative cognitive development, and behavioral outcome on the IQ level was found. All groups slightly improved in attention while memory functions tended to decrease and executive functions were stable. School placement remained unchanged for the majority of patients. Between 67 and 78% were seizure free 1 year after surgery (Engel outcome class I). CONCLUSIONS: IQ alone is not a good predictor of postoperative outcome in pediatric patients with epilepsy. As with patients of average-range intelligence, the decision to operate on patients with a low level of intelligence should depend on the results of the presurgical diagnostics. If the results of the neuropsychological examination indicate diffuse functional impairment, this should not hinder further steps, if all other findings are consistent.

Achievement↗
PubMed2005

Modeling clinical judgment and implicit guideline compliance in the diagnosis of melanomas using machine learning.

We explore several machine learning techniques to model clinical decision making of 6 dermatologists in the clinical task of melanoma diagnosis of 177 pigmented skin lesions (76 malignant, 101 benign). In particular we apply Support Vector Machine (SVM) classifiers to model clinician judgments, Markov Blanket and SVM feature selection to eliminate clinical features that are effectively ignored by the dermatologists, and a novel explanation technique whereby regression tree induction is run on the reduced SVM model's output to explain the physicians' implicit patterns of decision making. Our main findings include: (a) clinician judgments can be accurately predicted, (b) subtle decision making rules are revealed enabling the explanation of differences of opinion among physicians, and (c) physician judgment is non-compliant with the diagnostic guidelines that physicians self-report as guiding their decision making.

Artificial Intelligence↗
PubMed1985

Decision support systems for the outpatient office. Two examples of medical expert systems.

Although definitive decision-making systems remain beyond the scope of present-day applications available on the market, effective and efficient decision support systems have been developed and are entering the market place. These systems focus on very specific tasks in limited domains and can be valuable adjuncts in primary care settings. There are also a few large-scale applications with well-defined goals that have proved themselves. It is imperative to validate the organizational support and expertise underlying any expert decision support system.

Ambulatory Care↗
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&#x2019;s disease↗
PubMed1995

Bridging theory and practice: cognitive science and medical informatics.

Medical informatics has experienced dramatic growth, both as an applied and as a research discipline in recent years. In this paper, we argue that there is a need to expand the research base to characterize the cognitive dimension of informatics. Theories and methods from cognitive science can provide an effective counterpart to traditional medical informatics in addressing issues of usability of the systems, the processing of information, and the training of physicians. In the first part of the paper, we address the problems inherent in applying basic theories to practice and suggest some potential solutions. The second section deals with epistemological issues that are fundamental to cognitive science research and medical informatics. We then discuss two areas of application of cognitive scientific theories and methods to medical informatics: cognitive evaluation of human computer interface and intelligent medical decision support systems. The paper addresses the progress that has been made thus far and discusses how future cognitive research can facilitate further growth in the development of these applications.

Artificial Intelligence↗
PubMed2005

Individualizing generic decision models using assessments as evidence.

Complex decision models in expert systems often depend upon a number of utilities and subjective probabilities for an individual. Although these values can be estimated for entire populations or demographic subgroups, a model should be customized to the individual's specific parameter values. This process can be onerous and inefficient for practical decisions. We propose an interactive approach for incrementally improving our knowledge about a specific individual's parameter values, including utilities and probabilities, given a decision model and a prior joint probability distribution over the parameter values. We define the concept of value of elicitation and use it to determine dynamically the next most informative elicitation for a given individual. We evaluated the approach using an example model and demonstrate that we can improve the decision quality by focusing on those parameter values most material to the decision.

Algorithms↗
PubMed2000

[The electronic patient recrod: an essential part of using telematics in health care].

The DIADOQ CPR supports the explicit representation of the medical acts, facts and main inferences underlying the process of health care delivery. A controlled vocabulary is maintained by a frame-based concept system. This representation is the foundation for a consistent and comparable recording of patient data and for the flexible adaptation of screen forms to clinical contexts. The explicit and structured representation of the process of health care delivery enables the tight integration of knowledge-based decision support in the CPR system.

Artificial Intelligence↗
PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗
PubMed1996

[Computer-assisted medical decision].

A physician deals everyday with uncertainty to take decisions and treat patients. The goal of decision making systems is to facilitate and improve the decision. Classical approaches, such as decision trees or statistical tools, provide rates about possible actions and outcomes of physicians' decisions. Such information is essentially numerical. Other computer approaches, like expert systems, provide both numeric and symbolic information. The expert knowledge is embedded in the system and used to elaborate new decisions and to provide reasoning explanations. The use of computer making decision systems will certainly increase the performances in future medical activity as it will provide a reliable additional expert information to take decisions.

Algorithms↗
PubMed2001

Ranking of information in the computerized problem-oriented patient record.

We propose a framework for a problem-oriented patient record for general practice 1 and defend that the problem-oriented medical record represents an intuitive way to organize the patient record. By adding a layer of knowledge to the electronic patient record the record system is able to better utilize the information stored in the record. If a record system is process aware, having knowledge of work processes and is able to distinguish between different contexts in use, the system can provide relevant and useful information during the handling of patients' medical problems. Information is ranked according to its relevancy in a given context by using action patterns - traces. Traces give valuable indications of what is going on during the process of patient care. Decision frames represents relevant contexts based on the information in the record. Both decision frames and traces provide an environment in which more optimal medical decisions can be made.

Artificial Intelligence↗