Evidence map›Paper›PMID 41826720›Full record

SynthesisEuropean geriatric medicine2026

Research on patient-facing chatbots based on large language models in the care of older people: a living systematic review.

Jacob T Johnson, Jan J Duin, Tiberon Kuiper, Yvonne M Drewes, Jacobijn Gussekloo, Frederiek van den Bos, Armel E J L Lefebvre, Marco Spruit, Bram van Dijk, Simon P Mooijaart

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in European geriatric medicine, 2026. 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

10 authors.

Jacob T JohnsonDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. j.t.johnson@lumc.nl.ORCID http://orcid.org/0009-0001-3534-8264
Jan J DuinDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Tiberon KuiperDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Yvonne M DrewesDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Jacobijn GusseklooDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Frederiek van den BosDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Armel E J L LefebvreDepartment of Public Health and Primary Care, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Marco SpruitDepartment of Public Health and Primary Care, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Bram van DijkDepartment of Public Health and Primary Care, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Simon P MooijaartDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeChatbots based on large language models (LLMs), like ChatGPT, have promise to augment care of older people. The evidence base, however, is small. The aim of this systematic review is to study the evidence base of patient-facing LLM chatbots in the care of older people.

methodsFollowing the PRISMA guidelines, a systematic search was conducted in PubMed, Embase, Web of Science, CINAHL, and Cochrane Library up to 1st May 2025 (PROSPERO-CRD42025638985). Studies involving patient-facing LLM chatbots and patients aged 60 years or older were included. Characteristics of the study and chatbots were extracted including their technology readiness levels (TRL). The mixed methods appraisal tool (MMAT) was used to assess the quality of included studies.

resultsOut of 1228 records, 9 studies were included, with a median sample size of 12 older participants. Of these, 5 were mixed methods, 3 were qualitative, and 1 was a pilot/feasibility study. Seven studies evaluated chatbots providing supportive conversations or social/emotional support. GPT-3.5 (OpenAI) was the most frequently employed LLM. Median TRL was 7; no LLM chatbot was assessed at a TRL of 8 or higher (tested for effectiveness).

conclusionsFew studies report on patient-facing LLM chatbots in the care of older people. Most studies utilized qualitative methods. No study evaluated the clinical or cost-effectiveness of chatbots, and there were no studies conducted while the chatbot was fully implemented within clinical workflows. This indicates the research is still in its early exploratory phases. This living review will be updated periodically to follow developments in the field.

Indexed as

AgedHumansLarge Language ModelsArtificial intelligenceChatbotsLarge language modelsNatural language processingOlder people

Identifiers

PMID41826720
PMCPMC13309424

What Socratic holds

Textmetadata
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 Socratic graph.