Evidence map›Paper›PMID 34480711›Full record

ArticleMedicine, health care, and philosophy2022

Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care.

Jaana Parviainen, Juho Rantala

Abstract read
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In one paragraph

Article in Medicine, health care, and philosophy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

45 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  8. Evaluating ChatGPT-4o and DeepSeek-R1 for Patient Education in Nasopharyngeal Carcinoma Radiotherapy: a Comparative Analysis.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jaana ParviainenFaculty of Social Sciences, Tampere University, Tampere, Finland. jaana.parviainen@tuni.fi.ORCID http://orcid.org/0000-0002-7838-592X
Juho RantalaFaculty of Social Sciences, Tampere University, Tampere, Finland.ORCID http://orcid.org/0000-0002-6507-6749

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. The COVID-19 pandemic, however, has significantly increased the utilisation of health-oriented chatbots, for instance, as a conversational interface to answer questions, recommend care options, check symptoms and complete tasks such as booking appointments. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence clinical practices and expert-client relationships. We suggest the need for new approaches in professional ethics as the large-scale deployment of artificial intelligence may revolutionise professional decision-making and client-expert interaction in healthcare organisations. We argue that the implementation of chatbots amplifies the project of rationality and automation in clinical practice and alters traditional decision-making practices based on epistemic probability and prudence. This article contributes to the discussion on the ethical challenges posed by chatbots from the perspective of healthcare professional ethics.

Indexed as

Artificial IntelligenceCOVID-19Delivery of Health CareHumansPandemicsReferral and ConsultationSARS-CoV-2ChatbotCOVID-19ExpertiseHealth careProfessional ethics

Identifiers

PMID34480711
PMCPMC8416570

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.