ReviewCritical care clinics2022
Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.
Review in Critical care clinics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining the Role of AI in Changing the Role of Nurses in Patient Care: Systematic Review.JMIR nursing · 2025Pooled it
- Diagnostic errors in critically ill patients: challenges, contributing factors, and improvement strategies through the SEIPS framework.Intensive care medicine · 2026Article
- Meeting the challenges of electronic health record (EHR) optimization.NPJ digital medicine · 2025Review
- Health Care Providers' Perspectives of Clinical Decision Support Tools for Pediatric Sepsis in Bangladesh: Qualitative Study.JMIR formative research · 2025Article
- Accuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Invasive and non-invasive monitoring in the ICU.Trauma surgery & acute care open · 2025Review
- Analysis of CME Participation and Credit Compliance Among Healthcare Professionals: A Five-Year Study (2019-2023) From a Hospital in Guangdong, China.Advances in medical education and practice · 2025Article
- Ten misconceptions regarding decision-making in critical care.World journal of critical care medicine · 2024Review
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- Artificial Intelligence in Medicine and Dentistry.Acta stomatologica Croatica · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.