Bronchoscopicaccess to peripheral pulmonary lesions has been transformed by navigation, robotic, and imaging-assisted platforms, yet the physical interaction between an instrument tip and airway tissue remains difficult to quantify directly.Conventional guidewires provide push ability, torque transmission, and mechanical steering, but generally do not return a direct quantitative signal describing tissue interaction.This paper presents a literature-grounded feasibility framework that reimagines the guidewire as a multimodal sensing platform rather than a purely mechanical component.Four complementary sensing channelsforce, localized pressure, electrical impedance, and temperature-are proposed within a guidewire-centred architecture.The methodology comprises channel calibration and signal conditioning, temporal synchronization, feature extraction, lightweight edgeassisted sensor fusion, confidence estimation, and human-in-the-loop clinician feedback.Artificial intelligence is deliberately positioned as a supporting interpretation layer for procedural interaction states rather than as an autonomous cancer-diagnosis mechanism.The study analyses the principal mechanical, electrical, miniaturization, packaging, safety, and manufacturing constraints and defines a staged validation pathway from component calibration and bench testing through tissue-mimicking phantom evaluation, AI validation, preclinical feasibility, and eventual clinical feasibility.No prototype measurements or clinical diagnostic performance are claimed in the present work.The principal outcome is a coherent, experimentally testable architecture and validation roadmap that identifies how quantitative instrumenttissue information could complement existing bronchoscopic navigation while preserving clinician control.Advanced technologies are retained as future extensions outside the present scope. Index Terms-bronchoscopic intervention,
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Chaudhari et al. (2026) studied this question.
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