RT Journal Article
T1 Safety-First Framework for AI-Enabled Anamnesis in Head and Neck Surgery: Evidence Synthesis from a Narrative Review
A1 Vaira, Luigi Angelo
A1 Qadeer, Hareem
A1 Lechien, Jerome R.
A1 Maniaci, Antonino
A1 Maglitto, Fabio
A1 Troise, Stefania
A1 Chiesa-Estomba, Carlos M.
A1 Consorti, Giuseppe
A1 Cirignaco, Giulio
A1 Iannella, Giannicola
A1 Navarro Cuéllar, Carlos
A1 Salzano, Giovanni
A1 Soro, Giovanni Maria
A1 Boscolo Rizz, Paolo
A1 Vellone, Valentino
A1 Riu, Giacomo De
AB Objectives: To synthesize evidence on artificial intelligence (AI)-enabled medical history taking (anamnesis)—beyond large language models (LLMs) alone—and to translate findings into implications and research priorities for head and neck surgery. Methods: We performed a PRISMA-informed narrative review. Searches from database inception to 31 December 2025 (updated 3 January 2026) were conducted in MEDLINE (PubMed), Embase, Scopus, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library, supplemented by medRxiv/arXiv screening and citation chasing. We included studies evaluating or describing AI-supported history capture/summarization, conversational interviewing, symptom checker/digital triage, EHR-integrated intake-to-decision support pipelines, voice interviewing, education/training systems, and governance/ethical considerations related to digital anamnesis. Findings were synthesized by system category and by cross-cutting outcome domains, with a head and neck surgery interpretive lens. Results: Fifty studies (2014–2025) were included. Evidence most consistently suggested feasibility and acceptability of pre-consultation computer-assisted history taking and the potential to reduce documentation burden and improve structured capture. In contrast, symptom checkers and digital triage tools showed highly variable diagnostic/triage performance and prominent safety concerns, highlighting the importance of conservative red-flag escalation strategies, continuous monitoring, and clear accountability. LLM-based diagnostic dialogue demonstrated strong performance in controlled evaluations, but prospective real-world validation, governance, and workflow integration remain limited. Conclusions: AI-enabled anamnesis comprises heterogeneous tools with uneven evidence. For head and neck surgery, potential near-term applications may include structured pre-visit intake, clinician-facing summarization, and training applications, whereas autonomous triage warrants harm-oriented, specialty-calibrated validation and robust governance prior to broader clinical reliance.
PB MDPI
YR 2026
FD 2026-03-14
LK https://hdl.handle.net/20.500.14352/134159
UL https://hdl.handle.net/20.500.14352/134159
LA eng
NO Vaira LA, Qadeer H, Lechien JR, Maniaci A, Maglitto F, Troise S, et al. Safety-First Framework for AI-Enabled Anamnesis in Head and Neck Surgery: Evidence Synthesis from a Narrative Review. JCM 2026;15:2218. https://doi.org/10.3390/jcm15062218.
DS Docta Complutense
RD 9 abr 2026