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At least 19 recordsLinked to original sources
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↗
PubMed2026

AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.

BACKGROUND: CRISPR-based genetic screening has become a central methodology in functional genomics, enabling systematic interrogation of gene function, genetic interactions and context-dependent vulnerabilities at scale. However, the rapid expansion of screening modalities-including multi-condition designs, combinatorial perturbations, in vivo applications and single-cell readouts-has exposed fundamental limitations of heuristic-driven experimental design and post hoc statistical analysis. MAIN BODY: This Review synthesizes how artificial intelligence is reshaping CRISPR screening by introducing predictive, adaptive and system-level intelligence across the experimental lifecycle. We organize recent advances into two tightly coupled modules. First, machine learning and deep learning (ML/DL) methods optimize experimental design by learning context-dependent perturbation behavior, anticipating confounding effects and enabling iterative, information-efficient screening strategies. Second, large language model-agent (LLM-agent) systems complement these advances by externalizing scientific reasoning, integrating biological knowledge at scale and coordinating analysis and decision-making in human-in-the-loop workflows. CONCLUSIONS: Together, ML/DL and LLM-agent approaches reframe CRISPR screening from a static analytical pipeline into an intelligent experimental system, with important implications for robustness, scalability and biological discovery.

Artificial Intelligence↗
PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗
PubMed1992

Decision analysis for periodontal therapy.

Current decision-making approaches in clinical medicine and dentistry are based on principles developed when diagnostic and therapeutic options were few. The rapid pace of new technology development and the role of third-party payment systems are increasingly requiring that health-care providers and patients confront very complex decisions. These decisions typically involve significant uncertainty (e.g., How will a specific patient respond to each possible treatment?) and difficult tradeoffs (e.g., How much are patients willing to pay in money, time, and treatment effectiveness to avoid pain and discomfort?). This paper discusses high-quality decision-making. High-quality diagnostic and therapeutic decisions result from a well-developed decision basis that represents the alternatives, information, and preferences pertaining to the decision at hand. An effective decision basis is framed to address patients' and clinicians' key concerns. The resulting recommendation for action is based on an understanding of what factors are most sensitive in determining the best course of action. Moreover, the value of additional information-gathering efforts (e.g., further diagnosis) can be measured before the information is obtained to determine whether it is worth more than it costs. The paper illustrates the need for better decision-making methods with a sample patient case, discusses key decision analysis principles and methods, identifies specific areas where periodontal decision-making can be improved by decision analysis, and presents a periodontal decision analysis case. It then discusses how intelligent decision system technology can make decision analysis widely available in a clinical setting and concludes by exploring how a future dental office might use this technology on a routine basis.

Decision Making↗
PubMed1992

Dentists, drugs, and decisions: introduction to Part I--Therapeutic drugs.

Numerous dental therapeutic agents or drugs are available over the counter or with prescription for patient use at home or in a professional office. Knowledge of the processes by which these agents are evaluated for safety and efficacy by the FDA and ADA is necessary for dental professionals to make intelligent decisions concerning their use. Several examples of problems with drug/cosmetic evaluations are cited.

American Dental Association↗
PubMed1975

Results of pulmonary arterial banding in infancy. Survey of 5 years' experience in the New England Regional Infant Cardiac Program.

The results of pulmonary arterial banding in 238 infants, 12 percent of the infants admitted to the New England Regional Infant Cardiac Program, is reviewed. Overall survival to age 1 year was 63 percent. Survival was least likely (37 percent) in those who required banding within the 1st month of life. Additional surgery decreased the survival rate in those operated on after 1 month of age. Infants with anomalies for which no corrective surgical procedure is available (23 of 238) have only a 30 percent chance of survival. Those with lesions correctable within the 1st year (133 of 238) have a 74 percent survival rate; 52 percent (82 of 238) of those for whom a curative operation is available after the 1st year survive. These pulmonary arterial banding data coupled with results of primary correction should provide the data base required for an intelligent decision in respect to appropriate surgical treatment of infants with critical heart disease.

Heart Defects, Congenital↗
PubMed1991

Managing patient education: a perspective for the 1990s.

Changes in the delivery and complexity of health care make it imperative that the patient and family are provided with the information needed to make intelligent decisions and choices about health care alternatives. The intent of this paper is to outline the nurse manager's responsibilities for patient education and to provide a practical framework by which to structure patient teaching programs. Although the philosophies of self-care and self-determination are outlined and provide the primary orientation to the concept of patient education, the description of the nurse manager's clinical and aggregate skills provide both the training and practice guidelines.

Assertiveness↗
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↗
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↗
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↗
PubMed1991

Representation requirements for supporting knowledge-based construction of decision models in medicine.

This paper analyzes the medical knowledge required for formulating decision models in the domain of pulmonary infectious diseases (PIDs) with acquired immunodeficiency syndrome (AIDS). Aiming to support dynamic decision-modeling, the knowledge characterization focuses on the ontology of the clinical decision problem. Relevant inference patterns and knowledge types are identified.

Acquired Immunodeficiency Syndrome↗
PubMed1991

INFORM: integrated support for decisions and activities in intensive care.

Many medical decision support systems that have been developed in the past have failed to enter routine clinical practice. Often this is because the developers have failed to analyse in sufficient detail the precise user requirements, because they have produced a system which takes too narrow a view of the patient, or because the decision support facilities have not been sufficiently well integrated into the routine clinical data handling activities. In this paper we discuss how the AIM-INFORM project is setting out to deal with these issues, in the context of the provision of decision support in the intensive care unit.

Artificial Intelligence↗
PubMed1991

Managing Medical Logic Modules.

A key element of IAIMS development at the Columbia Presbyterian Medical Center (CPMC) is the Medical Logic Module (MLM), designed to provide decision support to clinical users. A standard has been established for MLMs, and a number of institutions have agreed in principle to share them. At CPMC, MLMs are under development and MLMs from other institutions are being reviewed. The Columbia Health Sciences Library has developed a management system for MLMs which supports both internal development and sharing of MLMs among institutions. This paper describes the elements of the MLM management system.

Artificial Intelligence↗
PubMed1992

An object oriented approach to interpret medical knowledge based on the Arden syntax.

A method is presented where medical knowledge modules, written in the Arden Syntax, are used in a decision-support system (DSS). Knowledge modules are, after syntax-checking, translated into the object oriented programming language C++, compiled and linked to the DSS. The object oriented approach together with developed tools, such as knowledge editor and translator, makes it possible to implement the Arden Syntax and to get an efficient, easy-maintained DSS. Work on a prototype shows that this approach has several advantages when building a DSS where medical knowledge is represented in the Arden Syntax.

Artificial Intelligence↗
PubMed1991

Ranking radiotherapy treatment plans using decision-analytic and heuristic techniques.

Radiotherapy treatment optimization is done by generating a set of tentative treatment plans, evaluating them and selecting the plan closest to achieving a set of conflicting treatment objectives. The evaluation of potential plans involves making tradeoffs among competing possible outcomes. Multiattribute decision theory provides a framework for specifying such tradeoffs and using them to select optimal actions. Using these concepts, we have developed a plan-ranking model which ranks a set of tentative treatment plans from best to worst. Heuristics are used to refine this model so that it reflects the clinical condition of the patient being treated and the practice preferences of the physician prescribing the treatment. A figure of merit is computed for each tentative plan, and is used to rank the plans. The approach described is very general and can be used for other medical domains having similar characteristics. The figure of merit can also be used as an objective function by computer programs that attempt to automatically generate an optimal treatment plan.

Artificial Intelligence↗
PubMed1991

The Columbia-Presbyterian Medical Center decision-support system as a model for implementing the Arden Syntax.

Columbia-Presbyterian Medical Center is implementing a decision-support system based on the Arden Syntax for Medical Logic Modules (MLM's). The system uses a compiler-interpreter pair. MLM's are first compiled into pseudo-codes, which are instructions for a virtual machine. The MLM's are then executed using an interpreter that emulates the virtual machine. This design has resulted in increased portability, easier debugging and verification, and more compact compiled MLM's. The time spent interpreting the MLM pseudo-codes has been found to be insignificant compared to database accesses. The compiler, which is written using the tools "lex" and "yacc," optimizes MLM's by minimizing the number of database accesses. The interpreter emulates a stack-oriented machine. A phased implementation of the syntax was used to speed the development of the system.

Artificial Intelligence↗