ReviewDrug safety2022
Intelligent Telehealth in Pharmacovigilance: A Future Perspective.
Review in Drug safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed, 32 citations in OpenAlex.
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Toward more accurate adverse event attribution in multiple myeloma clinical trials.Blood cancer journal · 2026Review
- A review on adverse drug reaction related to medication in health sector: an account of what we have discovered and implemented-pharmacovigilance.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Gestational diabetes mellitus and its association with newborn thyroid screening: a population-based study.Endocrine connections · 2025Article
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- The association between gestational selective serotonin reuptake inhibitor (SSRI) treatment and newborn thyroid screen: a large-scale cohort study.BMC pediatrics · 2025Observational
- Adverse drug reaction signal detection via the long short-term memory model.Frontiers in pharmacology · 2025Article
- Pharmacovigilance - Technological Advancements, Recent Developments and Innovations.Current drug safety · 2025Review
- Artificial intelligence in community pharmacy practice: Pharmacists' perceptions, willingness to utilize, and barriers to implementation.Exploratory research in clinical and social pharmacy · 2024Article
- Encouraging dissemination of research on the use of artificial intelligence and related innovative technologies in clinical pharmacy practice and education: call for papers.International journal of clinical pharmacy · 2024Article
- Artificial intelligence in the field of pharmacy practice: A literature review.Exploratory research in clinical and social pharmacy · 2023Review
- The ISoP PatEG-SIG for Promoting Patient Engagement in Pharmacovigilance: A Change of Paradigm is Needed.Drug safety · 2023Article
- Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal.PloS one · 2023Article
- Patient-centered pharmacovigilance: priority actions from the inherited bleeding disorders community.Therapeutic advances in drug safety · 2023Review
- Artificial Intelligence and Machine Learning for Safe Medicines.Drug safety · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices.
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.