ArticleJMIR medical informatics2017
Clinical Note Creation, Binning, and Artificial Intelligence.
Article in JMIR medical informatics, 2017. 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.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- The Use of Ambient Dictation Artificial Intelligence in Clinical Spaces in Surgery: A Scoping Review.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- Transforming Cancer Care: A Narrative Review on Leveraging Artificial Intelligence to Advance Immunotherapy in Underserved Communities.Journal of clinical medicine · 2025Review
- AI-driven multimodal colorimetric analytics for biomedical and behavioral health diagnostics.Computational and structural biotechnology journal · 2025Review
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- Medical Note Quality Audit at the Vascular Surgical Unit Compared With the British Medical Journal (BMJ) Guidelines.Cureus · 2023Article
- Creating space for gut feelings in the diagnosis of cancer in primary care.The British journal of general practice : the journal of the Royal College of General Practitioners · 2022Article
- Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- Autonomous detection, grading, and reporting of postoperative complications using natural language processing.Surgery · 2018Article
- The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries.Health equity · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans.
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.