Evidence map›Paper›PMID 33806501›Full record

ArticleDiagnostics (Basel, Switzerland)2021

Ethical Implications of Alzheimer's Disease Prediction in Asymptomatic Individuals through Artificial Intelligence.

Frank Ursin, Cristian Timmermann, Florian Steger

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
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

11 citing papers in PubMed, 27 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Frank UrsinInstitute of the History, Philosophy and Ethics of Medicine, Ulm University, Parkstrasse 11, D-89073 Ulm, Germany.ORCID 0000-0002-9378-3811
Cristian TimmermannInstitute of the History, Philosophy and Ethics of Medicine, Ulm University, Parkstrasse 11, D-89073 Ulm, Germany.ORCID 0000-0001-7935-2823
Florian StegerInstitute of the History, Philosophy and Ethics of Medicine, Ulm University, Parkstrasse 11, D-89073 Ulm, Germany.
Universität Ulm · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomarker-based predictive tests for subjectively asymptomatic Alzheimer's disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction.

Indexed as

autonomyexplicabilityinformed consentjusticemachine learningpopulation screeningpreclinicalpre-symptomatic

Identifiers

PMID33806501
PMCPMC7998766
OpenAlexW3134327384

What OpenQuestion holds

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LicenceCC BY
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.