Today, chronic diseases – long-term conditions that require ongoing medical attention – are pushing the healthcare system to the brink of collapse. According to, more than two-thirds of all deaths in America are caused by just five chronic diseases: heart problems, cancer, stroke, chronic obstructive pulmonary disease, and diabetes. On top of that, the dwindling count of healthcare workers isn’t helping either.
This can be very handy for the treatment of chronic conditions, which require accurate detection at first go for long-term care. Plus, since this is an AI system at work – and not a human, the process of analyzing the data and producing the diagnoses is relatively faster. Imagine someone getting to know about their tumor within a matter of days instead of weeks. They can get started with the treatment right away – which may lead to better clinical outcomes.
In busy clinical environments, there may be delays before a radiologist can thoroughly assess X-ray images. However, integrating computer vision as a second pair of eyes can help a lot. It enables the prompt detection of misplacements, allowing for immediate attention and prioritization. In recent times, AI models have been highly accurate in detecting and locating catheters on radiographs.
Notably, similar results have also been found in AI-driven diagnosis of skin cancer, which is not only the most widespread oncology disease in the US but also one that is very difficult to detect due to the inherent variability of skin lesions. Afrom 2022, authored by Yinhao et al. and published in the Frontiers, demonstrated the success of AI in this domain by highlighting ML models outperformed the average capabilities of dermatologists and can help with the early detection of skin cancers.