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PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗
PubMed1994

A knowledge-based alarm system for monitoring cardiac operated patients--assessment of clinical performance.

An intelligent alarm system for the postoperative monitoring of cardiac surgery patients, which did not require any manual data entries, was tested in two phases. A clinician monitored at bedside the patients' recovery and verified clinically abnormal physiological states. After the first test with ten patients, the system's rulebase was upgraded and then tested with an additional 15 patients. The alarm system employed two PC/ATs and was programmed to give notice of four pathological states (hyperdynamic state, hypovolemic state, hypoventilation and left ventricular failure) at two levels of urgency (alarm and alert levels). The monitoring lasted 5.4 +/- 1.7 hours per patient (mean +/- S.D.), totalling 134.7 hours. The system alarmed 27 times during the first and 73 times during the second phase of the testing. The sensitivity of the alarms was 100% in both phases, and the specificities increased from 20.0% to 73.9% and from 59.1% to 70.0% for the alarms and the alerts, respectively. This computerized decision support system based exclusively on data available in the automatically collected data base had a low false positive rate and gave early warnings about pathological states in the homogeneous group of adult postoperative cardiac patients.

Adult↗
PubMed1994

A causal and temporal reasoning model and its use in drug therapy applications.

Superficial knowledge about drug effects and interactions may provide clinicians with only a limited support for the elaboration of therapy plans. Deeper knowledge of the mechanisms through which drugs produce their effects, together with their temporal constraints, should be modelled to predict the effects and interactions of their joint administration. The present paper describes a method for modelling such deep medical knowledge, together with its causal and temporal reasoning capabilities, and compares it with classical approaches to temporal and causal reasoning, namely in the context of drug treatment applications. This method extends a previous causal functional model by allowing the representation of quantitative knowledge, the explicit representation of time intervals, and a temporal reasoning technique, Simulation by Interval Constraining. The method is illustrated by a number of examples of basic drug metabolic mechanisms; and its future use in the development of decision support systems for complex drug therapy applications is discussed.

Artificial Intelligence↗
PubMed2001

A biologically inspired neural network for dynamic programming.

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

Algorithms↗
PubMed2003

Ensemble machine learning on gene expression data for cancer classification.

Whole genome RNA expression studies permit systematic approaches to understanding the correlation between gene expression profiles to disease states or different developmental stages of a cell. Microarray analysis provides quantitative information about the complete transcription profile of cells that facilitate drug and therapeutics development, disease diagnosis, and understanding in the basic cell biology. One of the challenges in microarray analysis, especially in cancerous gene expression profiles, is to identify genes or groups of genes that are highly expressed in tumour cells but not in normal cells and vice versa. Previously, we have shown that ensemble machine learning consistently performs well in classifying biological data. In this paper, we focus on three different supervised machine learning techniques in cancer classification, namely C4.5 decision tree, and bagged and boosted decision trees. We have performed classification tasks on seven publicly available cancerous microarray data and compared the classification/prediction performance of these methods. We have observed that ensemble learning (bagged and boosted decision trees) often performs better than single decision trees in this classification task.

Algorithms↗
PubMedDate not supplied

The use of machine learning program LERS-LB 2.5 in knowledge acquisition for expert system development in nursing.

LERS-LB (Learning from Examples using Rough Sets Lower Boundaries) is a computer program based on rough set theory for knowledge acquisition, which extracts patterns from real-world data in generating production rules for expert system development. From LERS-LB evaluation of an SPSS-X data file containing data for recovery room patients, it was concluded that both statistical data files and existing databases can be converted to decision-table format needed by LERS-LB, but it is less desirable to work with statistical files than a well-developed database. It was also concluded that choosing a well-developed database and checking it thoroughly for accuracy and completeness should be done before running LERS-LB, or other learning programs, to avoid problems with data errors. Using rough set theory and a technique called 'dropping conditions' LERS-LB offers, at least in theory, a possible method for identifying which data items are critical to nursing practice. Further research and continued LERS-LB program enhancements still may help with identifying critical data items versus redundant data for nursing practice. LERS-LB, and other learning programs, offer techniques which will help reduce the knowledge acquisition bottleneck in nursing expert system development. It is doubtful, however, that learning programs will eliminate the need for involving domain experts in evaluating rules and expert systems for clinical decision support.

Artificial Intelligence↗
PubMed1983

Project health care at Bellevue Hospital.

Project Health Care is a program which uses college students considering careers in health care, as summer volunteers in the Emergency Department of a large, municipal hospital. It began with a small group in 1981, and was expanded the following year to include 16 students. The program was designed to provide opportunities for service and observation in various out-patient settings, seminars, lectures and individual and group projects. The project benefitted all participants. For the Emergency Department staff it stimulated better communication with non-professionals; for the hospital it provided intelligent, enthusiastic volunteer help while aiding community relations, and for the volunteers it provided an opportunity to make educated career decisions.

Emergency Service, Hospital↗
PubMed2004

Evaluation of KNAVE-II: a tool for intelligent query and exploration of patient data.

We present the results of a preliminary evaluation of KNAVE-II, a distributed knowledge-based computational framework for visualization, interpretation, and exploration of longitudinal clinical data and of multiple levels of concepts derivable from these data. KNAVE-II uses a distributed architecture to access at run-time clinical time-oriented data, a domain-specific knowledge base containing properties of the clinical data, and a knowledge-based problem-solving method for computing on-the-fly interpretations of these data. The purpose of the evaluation was to compare efficiency and user satisfaction when answering clinical queries of variable complexity about clinical time-oriented data using KNAVE-II, versus using methods available in standard clinical settings: paper chart or electronic spreadsheet (ESS). Subjects answered high-complexity queries significantly faster using KNAVE-II than when using paper or ESS. User satisfaction with KNAVE-II was significantly superior compared to satisfaction using paper or ESS, based on a standard usability scale. Users also explicitly ranked KNAVE-II as superior to paper and the ESS.

Artificial Intelligence↗
PubMed1992

Treatment decisions in axillary node-negative breast cancer patients.

Treatment decisions must be made on 9000 axillary node-negative breast cancer patients each month in the United States. Which of these patients will benefit from adjuvant therapy is a major question. Valid methods are needed to distinguish those patients who are "cured" from those who will suffer a cancer recurrence. A complex network of prognostic variables enters into the treatment decision, together with a risk-versus-benefit assessment. We are using a neural-network-based form of artificial intelligence that, once "trained" with data representing an event and its outcome, can identify subsets of patients with low recurrence risks. Larger data sets are being evaluated with the hope of introducing the neural-network technique to routine clinical practice.

Breast Neoplasms↗
PubMed2006

Informatics for patient safety: a nursing research perspective.

In Crossing the Quality Chasm, the Institute of Medicine (IOM) Committee on Quality of Health Care in America identified the critical role of information technology in designing a health system that produces care that is "safe, effective, patient-centered, timely, efficient, and equitable" (Committee on Quality of Health Care in America, 2001, p. 164). A subsequent IOM report contends that improved information systems are essential to a new health care delivery system that "both prevents errors and learns from them when they occur" (Committee on Data Standards for Patient Safety, 2004, p. 1). This review specifically highlights the role of informatics processes and information technology in promoting patient safety and summarizes relevant nursing research. First, the components of an informatics infrastructure for patient safety are described within the context of the national framework for delivering consumer-centric and information-rich health care and using the National Health Information Infrastructure (NHII) (Thompson & Brailer, 2004). Second, relevant nursing research is summarized; this includes research studies that contributed to the development of selected infrastructure components as well as studies specifically focused on patient safety. Third, knowledge gaps and opportunities for nursing research are identified for each main topic. The health information technologies deployed as part of the national framework must support nursing practice in a manner that enables prevention of medical errors and promotion of patient safety and contributes to the development of practice-based nursing knowledge as well as best practices for patient safety. The seminal work that has been completed to date is necessary, but not sufficient, to achieve this objective.

Accidental Falls↗
PubMed1993

Female adolescent contraceptive decision making and risk taking.

In a modified replication of an earlier study, cognitive capacity, cognitive egocentrism, experience (sexual/contraceptive behaviors and attitudes), and demographic factors were assessed as predictors of decision making in a contraceptive use paradigm. Additionally, these factors were assessed as predictors of risk-taking behavior--"sensible" use of contraceptives. Sixty sexually active, unmarried females, ages 14-18, served as subjects. Cognitive capacity and cognitive egocentrism variables as well as age, grade, and ethnic status significantly predicted six of the seven decision-making variables. One cognitive capacity variable and one sexual/contraceptive behavior variable significantly predicted "sensible" use of contraceptives. These results are compared to the earlier study and discussed within the context of relevant literature.

Adolescent↗
PubMedDate not supplied

The role of Web agents in medical knowledge management.

The intelligent agent approach offers the promise of remaking a traditional enterprise into a learning organization, one that uses progressive information technology and information strategies to study its outcomes prospectively, to avoid repetition of past errors, and to improve its effectiveness continuously. This paper explores whether agent technology can fulfill this promise in health care, both in a general sense and in terms of knowledge management and performance support. A case study is presented to illustrate some lessons learned and the pitfalls that need to be avoided as the development of agents proceeds.

Artificial Intelligence↗
PubMed1998

TraumaTalk: content-to-speech generation for decision support at point of care.

Communicating information in clinical environments is a crucial concern for medical decision support systems. Some systems can provide this support through text output that can be read by the clinician either from a screen or hard copy. However, speech is often a more appropriate way of conveying information in cases where the decision maker's eyes are already committed to another task or in cases where the telephone is the mode of communication. Some systems synthesize speech directly from text, while others piece together bits of pre-recorded human speech. In either case, producing correct intonation is vital because intonation both aids the listener's comprehension and conveys discourse meaning not necessarily evident in the words alone. Although systems that use text-to-speech or human recorded speech segments sometimes attempt to provide good intonation, they are severely hampered by the fact that intonation spans entire clauses. Systems that connect phonemes, words, or phrases fail to capture this. This paper describes a content-to-speech system that provides spoken decision support for trauma care that is correctly intoned over full clauses.

Decision Support Systems, Clinical↗
PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗