Teaching Heart Sounds to Health ProfessionalsTeaching Heart Sounds to Health Professionals

Cardiac Sound Assessment with AI in 2023

Editor’s note:

Recently I have been preparing a presentation to Medical students on cardiac auscultation. I felt that some comments on the role of AI in assessing heart sounds and murmurs would be useful for them in the current climate of accelerating development of this technology. My comments for the students are below and are followed by a more detailed state of the art review kindly provided by my colleague Dr Robert Chen.

AI and technology in cardiac auscultation
The history and a good physical exam remains foundational to diagnosis and can direct appropriate testing. AI assessment of heart sounds can detect murmurs but NOT give a diagnosis (in 2023). In children murmurs are common and often normal!

Commercial products to record heart sounds are widespread but so far unproven to be accurate in diagnosing murmurs. Your ears don’t need technology to detect murmurs! Diagnosis of their cause can be learned but requires practice.

~ JF November 2023

Artificial Intelligence and Machine Learning Cannot Replace Human Auscultation Skills

Contributed by

Robert P-C Chen MD FRCPC

Pediatric Cardiologist, IWK Children’s Hospital, Halifax, Nova Scotia
Posted November 2023

Artificial intelligence (AI) and machine learning (ML) can potentially enhance the diagnosis and management of cardiac diseases. AI and ML can analyze heart sounds and murmurs which provide valuable information about cardiac structure and function and help detect various valvular and congenital heart diseases. However, the interpretation of heart sounds and murmurs requires skill and experience. Therefore, AI and ML to assist or automate the auscultation process may seem appealing, especially in the era of telemedicine and digital health.

Unfortunately, there are limitations associated with the use of AI and ML for the purposes of assessing heart sounds and murmurs including:


The performance of AI and ML models depends on the quality and quantity of the data used to train and evaluate them. However, obtaining high-quality and representative data of heart sounds and murmurs is not easy. Factors affect the recording1 and transmission of acoustic signals include background noise, patient movement, stethoscope position, device type, and signal processing methods2. There is also a lack of standardization and consensus on the annotation and classification of heart sounds and murmurs leading to inconsistency and variability in data labels3. Furthermore, rare cardiac diseases or diseases with overlapping features can limit the availability and diversity of the data4.


The development and validation of AI and ML models for the analysis of heart sounds and murmurs is a complex and challenging task involving multiple steps and components, such as feature extraction, feature selection, dimensionality reduction, classification, and evaluation5,6. Each of these steps and components can have different options and parameters, affecting the performance and robustness of the models. AI and ML models, especially the deep learning ones, are “black boxes”, meaning their internal workings and decision-making processes are not transparent or understandable7. This raises ethical and legal issues regarding accountability and liability of the model developers and users8, and the trust and acceptance of the model outputs by the clinicians and patients9.


The goal of using AI and ML for the analysis of heart sounds and murmurs is to improve the clinical outcomes and quality of life of the patients. However, the translation of AI and ML models from the research setting to clinical practice requires rigorous evaluation of their accuracy, reliability, generalizability, and usability10. The integration of AI and ML models into the clinical workflow and decision-making process must also respect human factors and values, such as the autonomy, privacy, and dignity of the patients, and the expertise, intuition, and judgment of the clinicians. AI and ML models should not replace the holistic assessment of patients, but rather complement and augment the existing diagnostic and therapeutic modalities.

In conclusion, AI and ML have the potential to revolutionize the field of cardiac auscultation, but there are significant challenges and limitations to address and overcome. The use of AI and ML for the purposes of assessing heart sounds and cardiac murmurs should be based on sound scientific principles, rigorous clinical evidence, and ethical and legal standards, and should aim to enhance the human aspects of health care, rather than replace them.


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