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Abstract/Summary
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<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>We investigated the potential of an imaging-aware GPT-4-based chatbot in providing diagnoses based on imaging descriptions of abdominal pathologies.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>Utilizing zero-shot learning via the LlamaIndex framework, GPT-4 was enhanced using the 96 documents from the Radiographics Top 10 Reading List on gastrointestinal imaging, creating a gastrointestinal imaging-aware chatbot (GIA-CB). To assess its diagnostic capability, 50 cases on a variety of abdominal pathologies were created, comprising radiological findings in fluoroscopy, MRI, and CT. We compared the GIA-CB to the generic GPT-4 chatbot (g-CB) in providing the primary and 2 additional differential diagnoses, using interpretations from senior-level radiologists as ground truth. The trustworthiness of the GIA-CB was evaluated by investigating the source documents as provided by the knowledge-retrieval mechanism. Mann–Whitney <jats:italic>U</jats:italic> test was employed.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>The GIA-CB demonstrated a high capability to identify the most appropriate differential diagnosis in 39/50 cases (78%), significantly surpassing the g-CB in 27/50 cases (54%) (<jats:italic>p</jats:italic> = 0.006). Notably, the GIA-CB offered the primary differential in the top 3 differential diagnoses in 45/50 cases (90%) <jats:italic>versus</jats:italic> g-CB with 37/50 cases (74%) (<jats:italic>p</jats:italic> = 0.022) and always with appropriate explanations. The median response time was 29.8 s for GIA-CB and 15.7 s for g-CB, and the mean cost per case was $0.15 and $0.02, respectively.</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>The GIA-CB not only provided an accurate diagnosis for gastrointestinal pathologies, but also direct access to source documents, providing insight into the decision-making process, a step towards trustworthy and explainable AI. Integrating context-specific data into AI models can support evidence-based clinical decision-making.</jats:p>
</jats:sec><jats:sec>
<jats:title>Relevance statement</jats:title>
<jats:p>A context-aware GPT-4 chatbot demonstrates high accuracy in providing differential diagnoses based on imaging descriptions, surpassing the generic GPT-4. It provided formulated rationale and source excerpts supporting the diagnoses, thus enhancing trustworthy decision-support.</jats:p>
</jats:sec><jats:sec>
<jats:title>Key points</jats:title>
<jats:p>• Knowledge retrieval enhances differential diagnoses in a gastrointestinal imaging-aware chatbot (GIA-CB).</jats:p>
<jats:p>• GIA-CB outperformed the generic counterpart, providing formulated rationale and source excerpts.</jats:p>
<jats:p>• GIA-CB has the potential to pave the way for AI-assisted decision support systems.</jats:p>
</jats:sec><jats:sec>
<jats:title>Graphical Abstract</jats:title>
</jats:sec>
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Hinweis
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DeepGreen-ID: 58dc8d672a974b65bfff7115e1885f4d ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search) ;
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