Quantum-Enhanced AI-Driven Multifunctional Portable Diagnostic Device: A Conceptual Framework for Precision Preliminary Cardiovascular and Respiratory Assessment

Conventional auscultation, still anchored in the acoustic stethoscope, remains constrained by ambient-noise sensitivity, inter-observer subjectivity, and limited quantitative reproducibility. This paper presents a conceptual, simulation-based framework for a portable diagnostic device that couples nitrogen-vacancy (NV) center magnetometry in diamond to artificial-intelligence-based signal processing, alongside acoustic phonocardiography and photoplethysmographic oxygen saturation (SpO₂) sensing in a single wearable platform. The framework is developed at three levels: (i) the physical and engineering principles of NV-based magnetocardiography (MCG) and their integration with classical sensing modalities; (ii) a reproducible, open-source simulation that quantifies what does—and, more importantly, what does not—follow from an assumed reduction in sensor noise floor; and (iii) translational, regulatory, and economic considerations. Under the explicit assumption that NV-based transduction reduces the effective noise amplitude by an order of magnitude relative to conventional piezoelectric transduction, the simulation yields a signal-to-noise ratio (SNR) of 19.5 dB for the quantum-assisted channel versus −0.5 dB for the conventional channel. We prove analytically, and confirm numerically, that the resulting 20.0 dB gain is exactly 20 log₁₀(σ_trad/σ_q) and is invariant to signal morphology, to bandpass design, and to whether SNR is evaluated before or after filtering: it restates the assumed noise ratio and is not an independent physiological finding. Three further analyses qualify it. First, the gain is preserved only when both channels share a common noise spectrum; introducing the 1/f knee characteristic of solid-state spin sensors into the NV channel erodes it to 17.5 dB at a 100 Hz corner in the 20–400 Hz auscultation band, and to 12.0 dB in the 0.5–100 Hz band that magnetocardiography actually requires. Second, at a task level the assumed gain confers no measurable benefit within the model: a genie-aided matched filter already detects the simulated murmur with area under the curve 1.000 on the conventional channel, and heart rate is recovered as 75.00 bpm (95% interval 74.86–75.19 bpm) on both channels. Third, all other performance figures reported here—classifier accuracy, regulatory timelines, and cost projections—are design targets drawn from the cited literature on comparable devices, not results from a physical prototype or from patient data, and this distinction is maintained from the abstract through the conclusion. We conclude that the physical basis for NV-based cardiac magnetometry is established in the biomagnetism literature, but that the specific acoustic-to-magnetic transduction chain, the biosensing layer, and the AI classification pipeline proposed here require dedicated empirical validation, and that the operative engineering target is low-frequency noise-spectrum control rather than broadband noise-floor reduction.