The lung speaks out-and machines are listening.

BACKGROUND: Listening to audible sound for medical diagnosis and monitoring spans health care professionals, physiologists, psychologists, and computer engineers. Artificial intelligence has advanced acoustic analysis in both voice/speech science and respiratory medicine, but asymmetrically. This asymmetry has deep roots: McKusick's adoption of sound spectrography and Forgacs' oscilloscope-based waveform inspection applied methods established in voice/speech acoustics. The same one-way pattern persists: sound-classification methods migrate from voice/speech research into lung sound analysis with a multi-year lag. PURPOSE: I argue this transfer carried speech science's engineering strengths while omitting an achievement of classical respiratory acoustics: analysis referenced to airflow. Flow-gated phonopneumography established that lung sounds must be interpreted relative to the mechanical act producing them, a validated principle that is absent from contemporary AI-supported auscultation. I trace this omission from Laennec through 19th-century mechanical/acoustic conflations to self-supervised learning, showing how label ontology and missing physiological referencing constrain what these systems can learn, explain, or teach. CONCLUSIONS: Restoring flow-referenced analysis is not a return to outdated methodology but a correction to a field that adopted a neighboring discipline's instrumentation without its foundational insight. Parallels-annotator-quality gaps and physiology-referenced efforts, where voice/speech science is neither ahead nor behind-suggest that respiratory and voice/speech acoustics could collaborate to mutual benefit. Lung sound AI's next generation should be built on physiological grounding rather than acoustic pattern-matching alone, a principle also for all professionals who use their auditory perception in assisting health care and for the computer engineers building the tools that listen alongside them.