Evidence map›Paper›PMID 39417792›Full record

ArticleJMIR mental health2024

An Ethical Perspective on the Democratization of Mental Health With Generative AI.

Zohar Elyoseph, Tamar Gur, Yuval Haber, Tomer Simon, Tal Angert, Yuval Navon, Amir Tal, Oren Asman

Abstract read
In one paragraph

Article in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article
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

8 authors.

Zohar ElyosephDepartment of Brain Sciences, Faculty of Medicine, Imperial College, Fulham Palace Rd, London, W6 8RF, United Kingdom, 44 547836088.ORCID 0000-0002-5717-4074
Tamar GurThe Adelson School of Entrepreneurship, Reichman University, Herzliya, Israel.ORCID 0000-0002-5481-1832
Yuval HaberThe PhD Program of Hermeneutics & Cultural Studies, Bar-Ilan University, Ramat Gan, Israel.ORCID 0000-0003-4933-2113
Tomer SimonMicrosoft Israel R&D Center, Tel Aviv, Israel.ORCID 0000-0002-3736-9157
Tal AngertSagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.ORCID 0009-0004-0533-3928
Yuval NavonSagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.ORCID 0009-0005-3631-759X
Amir TalSamueli Initiative for Responsible AI in Medicine, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.ORCID 0000-0002-6099-8904
Oren AsmanSamueli Initiative for Responsible AI in Medicine, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.ORCID 0000-0003-2439-6997

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Knowledge has become more open and accessible to a large audience with the "democratization of information" facilitated by technology. This paper provides a sociohistorical perspective for the theme issue "Responsible Design, Integration, and Use of Generative AI in Mental Health." It evaluates ethical considerations in using generative artificial intelligence (GenAI) for the democratization of mental health knowledge and practice. It explores the historical context of democratizing information, transitioning from restricted access to widespread availability due to the internet, open-source movements, and most recently, GenAI technologies such as large language models. The paper highlights why GenAI technologies represent a new phase in the democratization movement, offering unparalleled access to highly advanced technology as well as information. In the realm of mental health, this requires delicate and nuanced ethical deliberation. Including GenAI in mental health may allow, among other things, improved accessibility to mental health care, personalized responses, and conceptual flexibility, and could facilitate a flattening of traditional hierarchies between health care providers and patients. At the same time, it also entails significant risks and challenges that must be carefully addressed. To navigate these complexities, the paper proposes a strategic questionnaire for assessing artificial intelligence-based mental health applications. This tool evaluates both the benefits and the risks, emphasizing the need for a balanced and ethical approach to GenAI integration in mental health. The paper calls for a cautious yet positive approach to GenAI in mental health, advocating for the active engagement of mental health professionals in guiding GenAI development. It emphasizes the importance of ensuring that GenAI advancements are not only technologically sound but also ethically grounded and patient-centered.

Indexed as

Artificial IntelligenceMental HealthDemocracyHumansaccessibilityAIChatGPTdigital mental healthethicsGenAIgenerative AIgenerative artificial intelligenceknowledgelarge language modelLLMmachine learningmental healthtechnology

Identifiers

PMID39417792
PMCPMC11500620

What OpenQuestion holds

Textmetadata
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.