Evidence map›Paper›PMID 40840932›Full record

ArticleHealthcare informatics research2025

How New Chatbots Can Support Personalized Medicine.

Leonardo J Ramírez López, Ana María Campos Mora

Abstract read
In one paragraph

Article in Healthcare informatics research, 2025. 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

2 authors.

Leonardo J Ramírez LópezTIGUM Research Group, Universidad Militar Nueva Granada, Bogota, Colombia.
Ana María Campos MoraTIGUM Research Group, Universidad Militar Nueva Granada, Bogota, Colombia.

Funding

Military University Nueva Granada INV-ING-3948, PIC-ING-3887
6 · The paper itself

Abstract

objectivesThis study proposes the integration of chatbots into personalized medicine by demonstrating how these tools can support the personalized medicine model. Chatbots can deliver tailored health recommendations, facilitate patient-doctor communication, and provide decision support in clinical settings. The goal is to establish a reference framework aligned with national and international standards for personalized healthcare solutions.

methodsThe chatbot model was developed by reviewing 30 scientific and academic articles focused on artificial intelligence and natural language processing in healthcare. The study analyzed the capabilities of existing healthcare chatbots, particularly their capacity to support personalized medicine through accurate data collection and processing of individual health information.

resultsKey parameters identified for effective chatbot deployment in personalized medicine include user engagement, data accuracy, adaptability, and regulatory compliance. The study established a compliance benchmark of 25% based on current industry standards and application performance. The results indicate that the proposed chatbot model significantly increased the precision and efficacy of personalized medical recommendations, surpassing baseline requirements set by standardization organizations.

conclusionsThis model provides healthcare professionals and patients with a robust framework for utilizing chatbots in personalized medicine, focusing on improved patient outcomes and engagement. The research identifies a gap in the application of artificial intelligence-driven tools in personalized healthcare and suggests strategic directions for future innovations. Implementing this model aims to bridge this gap, offering a standardized approach to developing chatbots that support personalized medicine.

Indexed as

Artificial IntelligenceHealth InformaticsNatural Language ProcessingPrecision MedicineTelemedicine

Identifiers

PMID40840932
PMCPMC12370424

What OpenQuestion holds

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LicenceCC BY-NC
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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 OpenQuestion graph.