Evidence map›Paper›PMID 34515809›Full record

ReviewDer Nervenarzt2021

[Digitalized psychiatry : Critical considerations on a new paradigm].

Thomas Fuchs

Abstract readReview
PubMed Publisher
In one paragraph

Review in Der Nervenarzt, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Thomas FuchsKarl-Jaspers-Professor für Philosophie und Psychiatrie, Psychiatrische Universitätsklinik, Voßstr. 4, 69115, Heidelberg, Deutschland. thomas.fuchs@urz.uni-heidelberg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digitalization and artificial intelligence hold the prospect of new procedures for psychiatry. Machine learning techniques combined with big data should enable algorithmized diagnostics, prediction and therapy that are superior to clinical observation and interaction in terms of accuracy and efficiency. This development converges with the search for new, statistically and biologically based disorder patterns to replace clinical psychopathological disease entities. This article examines the possibilities and the problems of a digitalized psychiatry.

Indexed as

Artificial IntelligencePsychiatryBig DataHumansMachine LearningPsychopathologyArtificial intelligenceDigital phenotypingInteractionPsychopathologySubjectivity

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.