Evidence map›Paper›PMID 42126692›Full record

ArticleJournal of medical systems2026

When Chatbots Become Agents: The Next Phase of Healthcare AI.

Nalan Karunanayake

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of medical systems, 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

1 author.

Nalan KarunanayakeDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. karunan@mskcc.org.ORCID http://orcid.org/0000-0003-4786-7165

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare artificial intelligence (AI) has moved beyond answering medical questions. In early 2026, OpenAI, Anthropic, and Google launched agentic systems that retrieve evidence, use tools, and execute multi-step workflows. These systems can integrate information across multiple clinical knowledge domains within a single workflow, but the evidence base for evaluating their reliability in clinical practice has not kept pace with deployment. This perspective argues that healthcare is adopting a technology evolving faster than anyone can evaluate, and that a brief window exists to shape integration before the architecture becomes entrenched. These systems share common architectural principles, yet only 19 prospective trials exist among over 4,600 clinical large language model studies, none evaluating agentic pipelines. The architecture is locking in, professional roles are being redefined, and builders hold a structural information advantage. Transparency, adaptive governance, and broader participation are needed before these choices become irreversible, and to ensure that the boundary between human clinical judgment and automated capability is drawn in a way that strengthens both. Every stakeholder in healthcare must engage with this shift now.

Indexed as

Artificial IntelligenceDigital HealthGenerative Artificial IntelligenceHumansLarge Language ModelsAgentic AIAI evaluationClinical decision supportHealthcare agentsLarge language models

Identifiers

What Socratic 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 Socratic graph.