ReviewJournal of medical systems2014
Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.
Review in Journal of medical systems, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
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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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review.PloS one · 2015Pooled it
- Use of artificial neural networks to identify the predictive factors of extracorporeal shock wave therapy treating patients with chronic plantar fasciitis.Scientific reports · 2019Trial
- Determining the appropriate amount of anesthetic gas using DWT and EMD combined with neural network.Journal of medical systems · 2015Trial
- User-Oriented Requirements for Artificial Intelligence-Based Clinical Decision Support Systems in Sepsis: Protocol for a Multimethod Research Project.JMIR research protocols · 2025Article
- Contrasting rule and machine learning based digital self triage systems in the USA.NPJ digital medicine · 2024Review
- Large Language Models in Healthcare: An Urgent Call for Ongoing, Rigorous Validation.Journal of medical systems · 2024Article
- Trustworthy and ethical AI-enabled cardiovascular care: a rapid review.BMC medical informatics and decision making · 2024Review
- Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis.Frontiers in psychology · 2024Article
- Breast cancer prediction using different machine learning methods applying multi factors.Journal of cancer research and clinical oncology · 2023Article
- On Approximating theBiomedicines · 2023Article
- [Characteristics of wideband tympanometry in patients with Ménière's disease based on neutral network].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2022Article
- Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam.Frontiers in public health · 2022Article
- On Outsourcing Artificial Neural Network Learning of Privacy-Sensitive Medical Data to the Cloud.International Conference on Tools with Artificial Intelligence : [proceedings]. International Conference on Tools for Artificial Intelligence · 2021Article
- Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.BMC medical informatics and decision making · 2021Article
- Integrated diagnostics: the future of laboratory medicine?Biochemia medica · 2020Review
- Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes.Frontiers in digital health · 2020Article
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.Database : the journal of biological databases and curation · 2020Article
- A Clinical Decision Support System for Predicting the Early Complications of One-Anastomosis Gastric Bypass Surgery.Obesity surgery · 2019Article
- Applications of artificial neural networks in health care organizational decision-making: A scoping review.PloS one · 2019Article
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
Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.
Indexed as
Identifiers
25027017What 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.