Evidence map›Paper›PMID 42080052›Full record

ReviewFrontiers in artificial intelligence2026

A structured framework for effective and responsible generative artificial intelligence chatbot prompt engineering throughout the scientific process: a comprehensive guide for the health and medical researcher.

Jeremy Y Ng

Abstract readReview
In one paragraph

Review in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Manuscript Writing: An Editorial Perspective.Journal of human reproductive sciences
    Review
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.

Jeremy Y NgInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence (GenAI) chatbots powered by large language models (LLMs) are becoming increasingly integrated into health and medical research workflows, offering researchers new tools to enhance efficiency, support innovation, and assist with knowledge translation. Although their use in health and medical research is expanding rapidly, the practical application of these tools across the broader health and medical research landscape remains complex and evolving. Health and medical researchers often engage with complex study designs, theoretical frameworks, and population needs, all of which require thoughtful, effective and responsible use when involving AI tools. This 10-chapter guide serves as a practical, evidence-informed resource for health and medical researchers to engage effectively and responsibly with GenAI chatbots through the practice of prompt engineering, the design of clear, structured, and purposeful prompts that guide GenAI chatbot outputs. It presents strategies to improve prompt quality and adapt GenAI chatbot interactions to the varied methodological and disciplinary contexts found across health and medical research. The article outlines a structured framework for how GenAI chatbots can be applied throughout the research cycle, including research question development, study design, literature searching, querying for appropriate reporting guidelines and appraisal tools, quantitative and qualitative data analysis, writing and dissemination, and implementation. AI-generated content should be treated as a preliminary draft and must always be reviewed, verified against credible sources, and aligned with disciplinary standards. Risks such as hallucinated content, embedded biases, and ethical challenges are addressed, particularly in sensitive or high-stakes settings. Transparency in AI use and researcher accountability are essential. While GenAI chatbots have the potential to expand access to research support and foster innovation, they cannot replace critical thinking, methodological rigour, or contextual understanding. Instead, they should augment, not replace, human expertise. This guide encourages effective and responsible use of GenAI chatbots and support their thoughtful integration into the health and medical research process.

Indexed as

artificial intelligencechatbotGenAI chatbotgenerative artificial intelligencemedical researchprompt engineeringresearch processscientific process

Identifiers

PMID42080052
PMCPMC13130486

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.