Artificial Intelligence or AI in Clinical Applications - Part 1

Cardiovascular:
Artificial intelligence algorithms have shown promising results in accurately diagnosing and risk stratifying patients with concern for coronary artery disease, showing potential as an initial triage tool. Other algorithms have been used in predicting patient mortality, medication effects, and adverse events following treatment for acute coronary syndrome. Wearables, smartphones, and internet-based technologies have also shown the ability to monitor patients' cardiac data points, expanding the amount of data and the various settings AI models can use and potentially enabling earlier detection of cardiac events occurring outside of the hospital. Another growing area of research is the utility of AI in classifying heart sounds and diagnosing valvular disease. Challenges of AI in cardiovascular medicine have included the limited data available to train machine learning models, such as limited data on social determinants of health as they pertain to cardiovascular disease.

A key limitation in early studies evaluating AI were omissions of data comparing algorithmic performance to humans. Examples of studies which assess AI performance relative to physicians includes how AI is non-inferior to humans in interpretation of cardiac echocardiograms and that AI can diagnose heart attack better than human physicians in the emergency setting, reducing both low-value testing and missed diagnoses.

In cardiovascular tissue engineering and organoid studies, AI is increasingly used to analyze microscopy images, and integrate electrophysiological read outs.

Dermatology:
Medical imaging (such as X-ray and photography) is a commonly used tool in dermatology and the development of deep learning has been strongly tied to image processing. Therefore, there is a natural fit between the dermatology and deep learning. Machine learning learning holds great potential to process these images for better diagnoses. Han et al. showed keratinocytic skin cancer detection from face photographs. Esteva et al. demonstrated dermatologist-level classification of skin cancer from lesion images. Noyan et al. demonstrated a convolutional neural network that achieved 94% accuracy at identifying skin cells from microscopic Tzanck smear images. A concern raised with this work is that it has not engaged with disparities related to skin color or differential treatment of patients with non-white skin tones.

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