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AI Assistant Generates Fundable, Clinically Relevant Research Questions Anchored to Real-World Data

AI converts clinical topics into fundable research ideas with methods, gaps, and real-world data.

by Jose Zea•3 min read

Generating Ideas for Research Topics in Healthcare

This use case addresses the challenge of efficiently generating high-impact, fundable research questions in healthcare. The objective is to assist research teams by transforming a medical condition or domain into actionable, innovative research ideas anchored to real-world data, recent literature, and clinical relevance, thus accelerating concept formation and enhancing the quality of study proposals.

Problem

Healthcare research teams often struggle to navigate the overwhelming volume of literature, fragmented trial registries, and the gap between clinical priorities and study design. This leads to wasted time on vague or duplicative ideas, poorly defined research questions, and outputs that appear creative but lack the specificity or feasibility required for funding or publication.

Problem Size

  • Evidence volume grows faster than clinicians and researchers can synthesize, causing scoping reviews to overlook key information.
  • Funding success rates are highest for proposals with clearly defined unmet needs, specific measurable outcomes, and feasible protocols, which are usually lacking in ad-hoc brainstorming sessions.
  • Poorly specified questions result in underpowered studies, weak or inappropriate endpoints, and irreproducible or unpublishable results.

Solution

  • An AI-powered assistant that translates a clinical condition or research domain into three high-value, clinically relevant research topics with clear significance.
  • The assistant proposes three innovative methodologies for each research topic, including data sources, study designs, analytic strategies, feasibility assessments, and ethical considerations.
  • It highlights and explains three explicit knowledge gaps, mapping each to a suitable testable study design and referencing recent literature and data sources to anchor the suggestions and prioritize novelty.

Opportunity Cost

  • Without such an AI assistant, teams risk redundant brainstorming, slow time-to-concept, misalignment with funder and IRB priorities, and wasted resources.
  • Utilizing the assistant enables faster, more rigorous idea generation, reduces duplication, and aligns concepts early on with fundable and clinically significant priorities.

Impact

  • Research ideas are produced in funder-ready formats, complete with clear target populations, comparators, outcomes, measurable endpoints, and feasibility assessments (e.g., sample size, effect size).
  • Faster progression from initial research topic to protocol draft, supporting more efficient grant writing and study initiation.
  • Greater differentiation and innovation by surfacing true knowledge gaps and avoiding well-trodden research areas through real-time trial and literature mapping.

This use case fosters research that is both clinically relevant and methodologically sound. By integrating up-to-date evidence, trial landscapes, and available real-world data, research teams can address critical gaps efficiently and ethically while anticipating the requirements of funders and review boards.

Data Sources

Recommended sources include PubMed and preprint servers for identifying recent studies; registries like ClinicalTrials.gov and ICTRP for ongoing/recent trials and identifying research saturation; guideline repositories (e.g., ACC/AHA, NICE) for clinical standards; epidemiological databases (e.g., Global Burden of Disease) for population insights; and institutional EHR/claims/registry datasets for feasibility. Methodology primers and systematic reviews support robust study design selection.

References

Prompt:

You are Arkangel AI’s Research Ideation Assistant. Goal: turn [medical condition] in [population/context, region] into precise, fundable, feasible research ideas grounded in recent evidence. If input lacks key details (population, setting, endpoints, available datasets, constraints), ask up to 3 clarifying questions before answering. Requirements (apply throughout): - Use last 5–7 years of evidence; name 3–6 high-signal sources (guidelines ACC/AHA/NICE/WHO, systematic reviews, large cohorts/trials, GBD) with year and DOI/URL/ID; cross-check novelty vs ClinicalTrials.gov/ICTRP and flag duplication (NCT/ISRCTN). - Be specific, clinically relevant, non-generic; avoid hand-waving; quantify where possible (baseline risk, plausible effect-size ranges from prior studies, sample-size ballpark with assumptions, timelines). - Map to available data (EHR/claims/registries/biobanks/trials), standard-of-care, and feasibility; note ethics, bias, and equity risks and mitigations. - Educational use only; no patient-specific advice; do not fabricate facts or citations—state uncertainty if evidence is thin. Use plain English and define acronyms on first use. Response structure: 1) Context (2–3 sentences): burden, unmet need, standard-of-care anchor. 2) Three Research Topics (≤100 words each): PICO one-liner; Significance (plain-English why-now). 3) Three Innovative Approaches (≤150 words each): study design; candidate data sources; analytic strategy (e.g., causal inference/RCT/adaptive/pragmatic); feasibility (sample-size assumptions, endpoints, follow-up, timelines); ethics/bias/equity. 4) Three Knowledge Gaps (≤100 words each): why the gap persists; proposed design to close it. 5) Evidence Anchors: 3–6 named sources + overlapping/ongoing trials. 6) Feasibility Snapshot: data availability, recruitment, key endpoints. 7) Assumptions and Uncertainties.