ReviewPostepy psychiatrii neurologii2022
The use of machine learning to support the therapeutic process - strengths and weaknesses.
Review in Postepy psychiatrii neurologii, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.BMC pregnancy and childbirth · 2025Article
- Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review.JMIR mental health · 2025Article
- Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices.Journal of personalized medicine · 2024Article
- In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.BMC medical informatics and decision making · 2023Article
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
Purpose: Artificial neural networks, "artificial intelligence" or machine learning now dominate a number of areas, making many activities automatic and thus affecting the safety and comfort of life. Neural networks might provide intelligent decisions with limited human assistance. Medicine also uses artificial intelligence, also in models designed to support the therapeutic process. The aim of this article is to define the main directions of development of machine learning applications in supporting the therapeutic processes. Views: Currently, the literature distinguishes at least a few applications of new technologies of varying degrees of advancement, with machine learning at the forefront [6]. It seems that the researchers are most interested in personalizing notifications of therapeutic applications, modifying therapeutic programs in a manner adapted to the patient's problems, and conducting "intelligent" conversations with them. Conclusions: There are dangers in using machine learning methods to support the therapeutic process. Particular attention should be paid to ensuring the full privacy of the implemented applications; moreover, selling user data of this type to third parties, such as those that sell certain medications or dietary supplements, would be ethically questionable. There are no legal regulations (or a system of recommendations of relevant scientific societies) that would limit proven applications to support the therapeutic process of a given disorder in the future, and which were created solely for the financial purpose of authors who did not conduct substantive consultations.
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.