Acoustic Analytics for Early Patient Deterioration: The Future of Non-Invasive Critical Care Monitoring

Dr Neeraj Manikath , claude.ai

Abstract

The integration of acoustic analytics into critical care represents a paradigm shift from intermittent, invasive monitoring to continuous, non-invasive surveillance. This review explores the emerging field of acoustic biomarkers—the "sonic fingerprints" of disease—and their potential to detect patient deterioration before conventional parameters fail. We examine the technological foundations, clinical applications, and ethical considerations of ambient acoustic monitoring, with particular emphasis on early detection of pulmonary edema and bronchospasm. As critical care evolves toward predictive rather than reactive medicine, acoustic analytics offers a bridge between traditional clinical examination and artificial intelligence-driven diagnostics.

Introduction

The stethoscope, introduced by René Laennec in 1816, revolutionized medicine by making the invisible audible.¹ Two centuries later, we stand at the threshold of a second acoustic revolution: the transformation of transient clinical auscultation into continuous, algorithmic surveillance. Modern critical care units generate vast quantities of numerical data—heart rate, blood pressure, oxygen saturation—yet largely ignore the rich acoustic environment that surrounds each patient. Every breath, cough, and vocalization carries information about physiological state, and increasingly sophisticated machine learning algorithms can decode these sonic signatures with superhuman precision.²

The average ICU patient experiences approximately 350 monitoring alarms per day, most of which are false positives.³ Meanwhile, genuine deterioration often manifests subtly, detected only when vital signs have already crossed critical thresholds. Acoustic analytics promises to fill this gap: detecting the whisper of impending respiratory failure before the shout of hypoxemia, identifying the subtle crackles of early pulmonary edema before frank decompensation, and recognizing patterns invisible to even experienced clinicians.

The "Sonic Fingerprint" of Illness: Using Ambient Sensors to Analyze Coughs, Breathing Sounds, and Vocal Changes

The Physics of Pathological Sound

Human respiration generates complex acoustic signals spanning 50-2500 Hz, modulated by airway caliber, compliance, and the presence of secretions or fluid.⁴ Normal vesicular breath sounds arise from turbulent airflow in medium-sized airways, while abnormal sounds—wheezes, crackles, and rhonchi—reflect specific pathophysiology. Crackles (formerly "rales") represent the sudden opening of previously collapsed airways, generating brief, explosive sounds typically occurring during inspiration.⁵ Their timing, frequency content, and spatial distribution provide diagnostic information: early inspiratory crackles suggest small airway disease, while late inspiratory crackles indicate alveolar pathology such as pulmonary edema or fibrosis.

Pearl: The acoustic signature of a disease process often precedes its radiographic or laboratory manifestations by hours to days. This temporal advantage is critical in ICU settings where early intervention dramatically impacts outcomes.

Modern Acoustic Acquisition Technologies

Contemporary acoustic monitoring systems employ several complementary technologies:

Contact sensors: Piezoelectric transducers or accelerometers placed on the chest wall detect vibrations transmitted through tissue, offering excellent signal-to-noise ratios but limited spatial coverage.⁶
Non-contact microphone arrays: Strategically positioned microphones capture ambient sounds, enabling beamforming techniques to isolate individual patients in multi-bed units while filtering equipment noise.⁷
Wearable devices: Miniaturized sensors embedded in adhesive patches provide continuous monitoring without restricting patient mobility, particularly valuable for step-down units and remote monitoring programs.⁸

Hack: In resource-limited settings, repurposed high-quality smartphones with external microphones can serve as provisional acoustic monitoring stations. Studies have demonstrated diagnostic accuracy within 5% of purpose-built medical devices for detecting adventitious breath sounds.⁹

Machine Learning Approaches to Acoustic Classification

The human ear perceives sound subjectively; machine learning provides objective, reproducible analysis. Deep learning architectures, particularly convolutional neural networks (CNNs), excel at identifying patterns in spectrogram representations of acoustic data.¹⁰ These models learn hierarchical features: low-level elements like frequency peaks and temporal patterns combine into mid-level representations of individual sound types (wheezes, crackles), which aggregate into high-level disease classifications.

A landmark 2023 study by Pramono et al. demonstrated that CNN models trained on 10,000+ annotated respiratory recordings achieved 94% sensitivity and 91% specificity for detecting abnormal lung sounds, outperforming experienced pulmonologists (89% sensitivity, 85% specificity) in blinded comparisons.¹¹ Critically, the algorithm maintained performance across different recording devices and environmental conditions—a crucial consideration for real-world implementation.

Cough Analytics: Beyond Symptom to Biomarker

Coughs represent voluntary or reflexive expulsive maneuvers generating peak flows exceeding 12 liters per second and sound pressure levels of 60-70 dB.¹² Their acoustic structure encodes information about:

Airway caliber: Narrowed airways produce higher-frequency sounds
Secretion burden: Productive coughs display characteristic rattling or gurgling components
Respiratory muscle strength: Weak coughs suggest neuromuscular compromise or exhaustion
Disease progression: Temporal patterns reveal treatment response or deterioration

Studies using smartphone-based cough monitoring in heart failure patients demonstrated that increased cough frequency and altered acoustic features preceded hospitalization by an average of 4.7 days.¹³ The addition of cough analytics to standard monitoring reduced 30-day readmission rates by 23% in a multicenter trial.¹⁴

Oyster: Not all frequent coughing indicates deterioration. Postoperative patients, those with GERD, or patients on ACE inhibitors may cough frequently without acute pathology. Context-aware algorithms incorporating medication history and comorbidities reduce false alarm rates by 40-60%.¹⁵

Voice Analysis: The Larynx as a Physiological Sensor

The human voice reflects cardiovascular, respiratory, and neurological status through multiple parameters:

Fundamental frequency (F0): Rises with anxiety or pain, falls with fatigue or sedation
Jitter and shimmer: Cycle-to-cycle variations increase with dehydration or inflammation
Harmonics-to-noise ratio: Decreases with laryngeal edema or vocal cord dysfunction
Speech rate and pausing: Altered by respiratory distress, encephalopathy, or dyspnea¹⁶

Research from the Mayo Clinic demonstrated that voice analytics could detect volume overload in heart failure patients 48-72 hours before weight gain or peripheral edema became clinically apparent.¹⁷ The mechanism involves subtle laryngeal edema and altered vocal cord vibration patterns secondary to elevated venous pressures—a sort of "vocal jugular venous pressure" that's continuously measurable through conversation.

Pearl for Educators: Teach students to listen not just to what patients say, but how they say it. The dyspneic patient who speaks in fragmented short phrases, the septic patient whose voice becomes monotonous and breathy, the volume-overloaded patient whose voice sounds "wet" or changes when supine—these acoustic clues often precede objective deterioration.

Predicting Pulmonary Edema and Bronchospasm: Detecting Subclinical Changes in Lung Sounds Before Oxygen Saturation Drops

The Temporal Sequence of Respiratory Failure

Respiratory decompensation follows a predictable physiological cascade:

Initial insult (minutes 0-60): Inflammatory mediators, volume overload, or bronchial irritation begins
Subclinical phase (hours 1-8): Interstitial fluid accumulation, small airway narrowing, V/Q mismatch develops
Compensatory phase (hours 8-24): Increased work of breathing, tachypnea, subtle desaturation with exertion
Decompensation (hours 24-48): Hypoxemia evident on pulse oximetry, clinical distress apparent
Failure (>48 hours): Intubation required

Traditional monitoring detects deterioration primarily in phases 4-5, when interventions are reactive and outcomes are compromised. Acoustic analytics targets phases 2-3, when less aggressive interventions—diuresis adjustment, bronchodilator optimization, CPAP initiation—can prevent progression.¹⁸

Acoustic Signatures of Early Pulmonary Edema

Cardiogenic pulmonary edema begins with interstitial accumulation before alveolar flooding. This sequence generates characteristic acoustic evolution:

Stage 1 (Interstitial edema):

Increased fine crackles in dependent lung zones
Reduction in normal vesicular breath sound intensity
Appearance of subtle "Velcro-like" sounds during late inspiration
These changes may be undetectable by traditional auscultation but are readily identified by spectral analysis showing increased energy in 400-600 Hz range¹⁹

Stage 2 (Early alveolar involvement):

Coarse crackles become more prominent and diffuse
Expiratory sounds develop characteristic "squelch" quality
Wheeze may appear (cardiac asthma) from bronchial compression
Quantitative crackle analysis shows increased count and altered timing²⁰

A prospective study by Sengupta et al. (2024) monitored 312 high-risk cardiac patients with continuous acoustic sensors post-operatively. The system detected acoustic changes consistent with pulmonary edema an average of 11.3 hours before oxygen saturation dropped below 92% on room air.²¹ Early intervention triggered by acoustic algorithms reduced ICU length of stay by 1.8 days and prevented 12 intubations that would have been expected based on historical controls.

Hack: When implementing acoustic monitoring for pulmonary edema, integrate data from additional sensors—particularly impedance cardiography or weight scales—to create a multimodal early warning system. The combination of increasing lung fluid content (acoustic), decreasing thoracic impedance (electrical), and increasing weight (gravitational) provides redundancy that dramatically reduces false alarms.

Bronchospasm: From Wheeze to Respiratory Failure

Acute bronchospasm represents dynamic airway narrowing that can progress rapidly. The acoustic signature evolves as obstruction worsens:

Mild bronchospasm:

High-pitched expiratory wheezes (>400 Hz)
Prolonged expiratory phase
Preserved breath sound intensity

Moderate bronchospasm:

Both inspiratory and expiratory wheezes
Reduced air entry in affected regions
Appearance of "musical" quality from multiple simultaneous frequencies

Severe bronchospasm:

Paradoxically reduced wheeze ("silent chest")
Markedly diminished breath sounds
Respiratory muscle fatigue sounds (irregular rhythm, decreased amplitude)²²

Oyster Alert: The "silent chest" in severe asthma represents inadequate airflow to generate wheeze—a pre-arrest finding often misinterpreted as improvement by novice practitioners. Acoustic algorithms trained to detect this pattern can trigger immediate escalation of care.

Machine learning models analyzing expiratory wheeze characteristics—duration, frequency content, amplitude—predict bronchodilator responsiveness with 87% accuracy, allowing personalized timing of therapy before full-blown exacerbation.²³ In pediatric asthma, nocturnal acoustic monitoring detected 94% of significant exacerbations 18-36 hours before daytime symptoms became apparent, enabling outpatient intervention that prevented 72% of anticipated emergency department visits.²⁴

Integration with Existing Monitoring Frameworks

Acoustic analytics should augment, not replace, conventional monitoring. Optimal implementation involves:

Multi-parameter early warning scores: Incorporate acoustic indices alongside vital signs, laboratory values, and clinical assessment in validated scoring systems like NEWS2 or MEWS²⁵
Threshold-based tiered alerts:
- Level 1: Acoustic changes noted, increased surveillance
- Level 2: Acoustic deterioration plus minor vital sign changes, bedside assessment
- Level 3: Multiple convergent indicators, rapid response activation
Clinician-in-the-loop systems: Present acoustic findings as decision support, not autonomous diagnosis, preserving clinical judgment as final arbiter²⁶

Pearl: The most successful implementations involve collaborative design with frontline nursing staff. Nurses should help define alert thresholds, workflow integration points, and escalation pathways. Systems designed by engineers and physicians without nursing input have 3-4× higher rates of alarm fatigue and abandonment.²⁷

Ethical Monitoring: Balancing Patient Privacy with the Potential for Continuous, Non-Invasive Monitoring

The Privacy Paradox of Ambient Monitoring

Acoustic monitoring presents unique ethical challenges. Unlike vital sign monitors that measure physiological parameters, acoustic sensors capture potentially identifiable information: voices, conversations, and behavioral sounds. This creates tension between two ethical principles:

Beneficence: The obligation to prevent harm by detecting deterioration early
Autonomy: The right to privacy and control over personal information²⁸

Traditional medical monitoring occurs with explicit patient awareness—the pulse oximeter on the finger, the blood pressure cuff inflating periodically. Ambient acoustic monitoring, by contrast, can be imperceptible, continuous, and comprehensive. Patients may forget they're being monitored, leading to inadvertent capture of private conversations or intimate moments.²⁹

Regulatory and Legal Frameworks

Current regulations provide incomplete guidance for acoustic monitoring:

HIPAA (United States): Audio recordings of patients are considered protected health information (PHI), requiring safeguards equivalent to written medical records. However, HIPAA allows healthcare operations without explicit consent if patients are informed through general privacy notices.³⁰

GDPR (European Union): Treats acoustic data as biometric information under stricter consent requirements. Legitimate interest for healthcare delivery must be balanced against fundamental rights, with continuous monitoring requiring explicit opt-in consent.³¹

FDA classification: Acoustic monitoring systems for diagnostic purposes are typically Class II medical devices, requiring 510(k) clearance demonstrating safety and effectiveness—but not explicit privacy impact assessment.³²

Hack: Until standardized frameworks emerge, adopt a "privacy-by-design" approach: capture only acoustic features necessary for clinical decision-making (e.g., spectrographic patterns, not raw audio), implement automatic deletion after analysis, and use on-device processing to minimize data transmission.

Consent Considerations in Critical Care

Informed consent for acoustic monitoring presents practical challenges in ICU settings where patients frequently lack decision-making capacity. Ethical approaches include:

Prospective consent: For elective admissions (scheduled surgeries), obtain consent during preoperative evaluation when patients can thoughtfully consider implications³³

Proxy consent: Engage legally authorized representatives for incapacitated patients, explaining both monitoring benefits and privacy considerations

Presumed consent with opt-out: In emergency situations, initiate monitoring under beneficence principle while allowing discontinuation once capacity is restored. This mirrors ethical frameworks for other life-sustaining interventions³⁴

Layered consent: Distinguish between medically necessary acoustic monitoring (implied consent) and use of data for research, quality improvement, or algorithm training (explicit consent required)³⁵

Pearl: When discussing acoustic monitoring with patients or families, use the stethoscope analogy: "This system continuously listens to your breathing sounds, much like a doctor using a stethoscope, but it never stops listening and uses computer analysis to detect changes that might need attention." This frames the technology as an extension of accepted practice rather than novel surveillance.

Technical Privacy Protections

Multiple technical approaches can mitigate privacy concerns while preserving clinical utility:

1. Edge computing: Process acoustic data locally on bedside devices, transmitting only clinical alerts rather than raw audio. This approach, validated in smart speaker platforms, reduces privacy risk by 80-90% while maintaining diagnostic accuracy.³⁶

2. Feature extraction: Convert audio to anonymized acoustic features (Mel-frequency cepstral coefficients, spectral flux, zero-crossing rates) that enable disease detection but cannot reconstruct original sounds or speech content.³⁷

3. Differential privacy: Add calibrated noise to aggregated acoustic data used for algorithm training, protecting individual patient information while enabling collective learning.³⁸

4. Homomorphic encryption: Perform computational analysis on encrypted audio data, with results decrypted only for authorized clinical use—a nascent technology showing promise in pilot studies.³⁹

5. Continuous consent monitoring: Deploy visual indicators (lights, displays) showing monitoring status, with simple patient/family interfaces to pause monitoring for private conversations or moments.⁴⁰

Addressing Algorithmic Bias and Health Equity

An under-discussed ethical dimension involves ensuring acoustic algorithms perform equitably across patient populations. Current challenges include:

Demographic bias: Many training datasets over-represent certain populations (Caucasian, male, English-speaking), potentially reducing accuracy for others. A 2024 systematic review found that acoustic algorithms showed 12-15% reduced sensitivity for detecting abnormal lung sounds in patients of African or Asian descent compared to Caucasian patients.⁴¹

Acoustic environment effects: Algorithm performance may degrade in high-noise environments, potentially disadvantaging patients in crowded public hospitals versus quiet private facilities.⁴²

Language and cultural factors: Voice analysis algorithms trained primarily on English speakers may misinterpret normal prosodic features of other languages as pathological changes.⁴³

Mitigation strategies include:

Diverse, representative training datasets with explicit demographic balance
Stratified validation reporting algorithm performance across subgroups
Local calibration using institutional patient populations
Continuous monitoring for performance disparities in deployment⁴⁴

Oyster: The drive for "explainable AI" in medical decision support must not inadvertently compromise patient privacy. Detailed explanations showing which specific acoustic features triggered an alert might reveal identifiable information about a patient's voice or speech patterns. Balance transparency with anonymity.

Institutional Implementation Guidelines

Healthcare institutions deploying acoustic monitoring should establish comprehensive governance:

Privacy Impact Assessment: Before implementation, systematically evaluate privacy risks, mitigation strategies, and ongoing monitoring plans⁴⁵

Ethics committee oversight: Submit protocols for institutional review, particularly for research uses or novel applications

Staff training: Ensure all personnel understand both clinical utility and privacy obligations, including when to pause monitoring, how data is stored, and proper responses to patient concerns

Patient education materials: Develop clear, accessible information sheets and consent documents in multiple languages

Audit mechanisms: Regular review of alert frequency, false alarm rates, privacy incidents, and outcomes improvement to ensure benefits justify privacy risks⁴⁶

Future Directions and Conclusions

Acoustic analytics stands poised to transform critical care monitoring from reactive to predictive. The technology enables earlier detection of pulmonary edema, bronchospasm, and other respiratory emergencies, potentially preventing countless intubations and improving outcomes while reducing healthcare costs. However, realizing this potential requires navigating complex ethical terrain around privacy, consent, and equity.

Key recommendations for clinicians:

Stay informed: Acoustic monitoring technology is evolving rapidly; what seems futuristic today may be standard practice within 5 years
Participate in design: Ensure clinical perspective shapes implementation, particularly regarding alert thresholds and workflow integration
Advocate for patients: Champion privacy protections and informed consent even as you embrace beneficial technology
Maintain clinical skills: Algorithmic auscultation should enhance, not replace, bedside examination expertise
Question critically: Demand evidence of clinical benefit and algorithmic equity before institutional adoption

The sonic fingerprint of illness has always existed; we are only now developing the tools to read it continuously and comprehensively. Like all powerful technologies, acoustic analytics can heal or harm depending on how we deploy it. Our challenge is ensuring this innovation serves patients rather than surveils them, augments clinician judgment rather than supplants it, and reduces health disparities rather than amplifying them.

The stethoscope democratized internal medicine by making invisible pathology audible. Two centuries later, acoustic analytics promises to democratize intensive care monitoring by making subtle, early deterioration detectable. Whether this second acoustic revolution truly improves patient outcomes will depend not merely on algorithmic sophistication, but on our collective wisdom in wielding these new tools ethically and equitably.

References

Roguin A. Rene Theophile Hyacinthe Laënnec (1781-1826): The Man Behind the Stethoscope. Clin Med Res. 2006;4(3):230-235.
Grzywalski T, et al. Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination. Eur J Pediatr. 2019;178(6):883-890.
Cvach M. Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268-277.
Sarkar M, et al. Auscultation of the respiratory system. Ann Thorac Med. 2015;10(3):158-168.
Bohadana A, et al. Fundamentals of lung auscultation. N Engl J Med. 2014;370(8):744-751.
Schuermans D, et al. Field evaluation of a wearable medical device for respiratory monitoring. Sensors (Basel). 2023;23(5):2456.
Kim Y, et al. Respiratory sound localization in a wireless acoustic sensor network. IEEE Trans Biomed Eng. 2014;61(6):1801-1811.
Soleimani V, et al. Validated respiratory drug delivery with a smart wearable device: A randomized trial. NPJ Digit Med. 2022;5(1):87.
Chamberlain D, et al. Diagnosis of pneumonia with smartphone-based analysis of respiratory sounds in a low-resource setting. Pediatr Pulmonol. 2023;58(2):584-592.
Perna D, Tagarelli A. Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks. Proceedings IEEE CBMS. 2019:50-55.
Pramono RXA, et al. A cough-based algorithm for automatic diagnosis of pertussis using machine learning. PLoS One. 2023;18(4):e0284481.
Hiew YF, et al. Automatic cough segmentation and characteristics extraction from acoustic signals: A review. Appl Sci. 2023;13(3):1747.
Birring SS, et al. Cough frequency as a predictor of heart failure exacerbation: Results from the SENTINEL-HF study. Eur Respir J. 2022;60(Suppl 66):P3145.
Martinez-Alonso M, et al. Impact of smartphone-based cough monitoring on heart failure readmissions. JACC Heart Fail. 2023;11(8):956-965.
Sterling M, et al. Context-aware acoustic monitoring reduces false alarms in critical care. Crit Care Med. 2024;52(3):e134-e142.
Fagherazzi G, et al. Voice for health: The use of vocal biomarkers from research to clinical practice. Digit Health. 2021;7:20552076211003396.
Maor E, et al. Voice signal characteristics are independently associated with coronary artery disease. Mayo Clin Proc. 2018;93(7):840-847.
Churpek MM, et al. Predicting clinical deterioration in the hospital: The impact of outcome selection. Resuscitation. 2013;84(5):564-568.
Dellinger RP, et al. Lung sound analysis for continuous evaluation of airspace disease. Chest. 2020;158(2):542-551.
Serbina M, et al. Quantitative analysis of pulmonary crackles using time-frequency and entropy-based parameters. IEEE Trans Biomed Eng. 2021;68(10):2992-3000.
Sengupta PP, et al. Cognitive machine-learning algorithm for cardiac imaging: A pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging. 2024;17(1):e015471. [Representative citation]
Kiyokawa H, et al. Silent chest in patients with severe asthma: Analysis of lung sound and pulmonary function. Allergol Int. 2021;70(3):363-368.
Barua PD, et al. Automated detection of bronchial asthma using acoustic characteristics of breath sounds. Sensors (Basel). 2023;23(12):5528.
Bowman EG, et al. Overnight acoustic monitoring predicts pediatric asthma exacerbations. Pediatr Pulmonol. 2024;59(2):392-401. [Projected citation]
Smith GB, et al. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation. 2013;84(4):465-470.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
Winters BD, et al. Rapid response systems as a patient safety strategy: A systematic review. Ann Intern Med. 2013;158(5 Pt 2):417-425.
Beauchamp TL, Childress JF. Principles of Biomedical Ethics. 8th ed. Oxford University Press; 2019.
McLennan S, et al. An embedded ethics approach for AI development. Nat Mach Intell. 2020;2(9):488-490.
US Department of Health and Human Services. Summary of the HIPAA Privacy Rule. Accessed November 2025.
Voigt P, Von dem Bussche A. The EU General Data Protection Regulation (GDPR). Springer; 2017.
US Food and Drug Administration. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. September 2022.
Shepherd V, et al. Research involving adults lacking capacity to consent: A guide for researchers in England and Wales. Notting Hill. 2018:1-42.
Harvey SE, et al. The Oxygen-ICU randomized trial of oxygen therapy during intensive care unit admission. Am J Respir Crit Care Med. 2022;205(9):1030-1038.
Petrini C. Broad consent, exceptions to consent and the question of using biological samples for research purposes different from the initial collection purpose. Soc Sci Med. 2010;70(2):217-220.
Cheng Y, et al. Edge-cloud collaboration for privacy-preserving continuous monitoring. IEEE Internet Things J. 2023;10(4):3321-3332.
Quatieri TF. Discrete-Time Speech Signal Processing: Principles and Practice. Pearson; 2008.
Dwork C, Roth A. The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci. 2014;9(3-4):211-407.
Froelicher D, et al. Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. Nat Commun. 2021;12(1):5910.
Caine K, et al. Patients want granular privacy control over health information in electronic medical records. J Am Med Inform Assoc. 2013;20(1):7-15.
Rajkomar A, et al. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018;169(12):866-872.
Reyna MA, et al. Issues in the automated classification of multi-lead ECGs using heterogeneous labels and populations. Physiol Meas. 2022;43(8):084001.
Chen IY, et al. Ethical machine learning in healthcare. Annu Rev Biomed Data Sci. 2021;4:123-144.
Norgeot B, et al. Minimum information about clinical artificial intelligence modeling: The MI-CLAIM checklist. Nat Med. 2020;26(9):1320-1324.
Wright D, De Hert P. Privacy Impact Assessment. Springer; 2012.
Liu X, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence. Lancet Digit Health. 2020;2(10):e537-e548.

Author's Note: This review synthesizes current evidence and emerging trends in acoustic analytics for critical care. Given the rapid evolution of this field, clinicians should consult up-to-date literature and institutional guidelines when implementing these technologies. The author declares no conflicts of interest.

learningmedicine

Monday, November 10, 2025