AI-powered ‘deep medicine’ could transform healthcare in the NHS and reconnect staff with their patients

• He outlines what he calls the deep medicine framework as a comprehensive strategy for the incorporation of AI into different aspects of healthcare.
• The framework of deep medicine is built upon three core pillars: deep phenotyping, deep learning and deep empathy.
• These pillars are all interconnected and adopting this framework could enhance patient care, support healthcare staff and strengthen the entire NHS system.

Deep phenotyping

• Deep phenotyping refers to a comprehensive picture of an individual’s health data, across a full lifetime.
• A deep phenotype goes far beyond the limited data collected during a standard medical appointment or health episode.

Deep learning

• This is where deep learning – an area of AI that seeks to simulate the decision-making power of the human brain – is so valuable.
• Deep learning uses an algorithm called a neural network that uses little, mathematical computers, called “neurons”, that are connected to one another to share and learn information.

• Advances in neural network algorithms, technology, and availability of digital data have enabled neural networks to demonstrate impressive performance.
• For instance, they have enabled the rapid and accurate analysis of medical images, such as X-rays and MRIs.
• In addition, AI technology like that behind ChatGPT can process medical literature and patient records to help make complex diagnoses.

Deep empathy

• This is the pillar of deep medicine known as deep empathy.
• Healthcare has increasingly become a discipline where the human touch, once its cornerstone, is overshadowed by a relentless pursuit of efficiency.
• AI solutions can be designed to reduce the administrative burdens for staff, opening up more opportunities for meaningful patient interaction.

Will Jones does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.