The NHS is under a lot of time pressure these days, which raises the risk of quick visits, misdiagnoses, and postponed therapy. Lack of resources and overworked staff worsen these issues, resulting in long patient wait times and typical treatment programs.

Employees can function using a cursory understanding of patient information, depending on recent test findings and basic medical histories. Their capacity to completely comprehend patient demands is hampered by the lack of thorough data, which also endanger the precision and individualization of diagnoses and therapies. Such healthcare approach, with its restrictions and involvements, is best described as “shallow medicine.”

The American cardiologist and scientist Eric Topol introduced the concept of “deep medicine” in his 2019 book Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. He critiques the US’s shallow medicine model, offering insights from his clinical and personal experiences.

Deep medicine holds the potential to revolutionise medical diagnostics, the effectiveness of treatments, and operational considerations. Topol presents artificial intelligence (AI) as the transformative solution to these systemic shallow issues. He outlines what he calls the deep medicine framework as a comprehensive strategy for the incorporation of AI into different aspects of healthcare.

Three fundamental pillars support the deep medicine framework: deep phenotyping, deep learning, and deep empathy. Adopting this paradigm could improve patient care, help medical professionals, and strengthen the NHS system as a whole because these pillars are interrelated.

Artificial intelligence (AI) technologies have the potential to lessen staff administrative workloads, allowing for more possibilities for meaningful patient connection. By taking down these barriers, one can concentrate more on providing direct patient care, which will enhance the standard of care and, ideally, increase patient happiness.