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

Today's NHS faces severe time constraints and poses the chance of short consultations and concerns in regards to the risk of misdiagnosis or delayed treatment. These challenges are further exacerbated by limited resources and overburdened staff, resulting in long wait times for patients and generic treatment strategies.

Staff can work with a superficial view of patient data, counting on basic medical histories and current test results. This lack of comprehensive data impacts their ability to totally understand patients' needs and compromises the accuracy and individualization of diagnoses and coverings. Such an approach to health, characterised by these limitations and obligations, might aptly be called “superficial medicine.”

American cardiologist and scientist Eric Topol introduced the concept of “deep medicine” in his 2019 book Deep Medicine: How artificial intelligence could make healthcare human again. He critiques the U.S.'s shallow medical model and offers insights from his clinical and private experiences.

Deep medicine holds the potential to revolutionize medical diagnostics, treatment effectiveness and surgical considerations. Topol presents artificial intelligence (AI) as a transformative solution to those systemic, superficial problems. He outlines what he calls the Deep Medicine Framework as a comprehensive strategy for integrating AI into various features of healthcare.

The basic framework of deep medicine relies on three basic pillars: deep phenotyping, deep learning and deep compassion. These pillars are all interconnected and the introduction of this framework could improve patient care, support healthcare staff and strengthen the broader NHS system.

In-depth phenotyping

Deep phenotyping is a comprehensive picture of a person's health data across a lifetime. A deep phenotype goes far beyond the limited data collected during a standard doctor's appointment or health episode. This includes things like an individual's genetic codean individual's entire DNA and knowledge in regards to the body's microbes or microbiome.

It includes the so-called “exposome”, the things within the environment that an individual is exposed to over the course of their life, equivalent to air pollution. It includes markers that reveal details in regards to the metabolic processes in an individual's body and the proteins their body expresses, in addition to other biological measures and metrics. It includes an individual's electronic health records, including their medical history, diagnoses, treatments and laboratory results.

Deep learning

The philosophy underlying deep phenotyping is to mix this diverse data to enable more accurate and rapid diagnoses, more precise and effective treatments, and advance predictive and preventive medical strategies. However, the sheer volume and complexity of knowledge collected pose significant challenges in evaluation. That's where deep learning – an area of ​​AI designed to simulate the decision-making power of the human brain – is so beneficial. Deep learning uses an algorithm called a neural network It uses computers which can be connected to one another to exchange information, much like nerve cells or neurons within the brain.

AI could potentially improve the usage of diagnostic tools.
Elif Bayraktar / Shutterstock

Advances in neural network algorithms, technology, and availability of digital data have enabled neural networks to display impressive performance. For example, they’ve enabled the rapid and accurate evaluation of medical images equivalent to X-rays and MRI images. You can generate reports and predict disease progression and patient outcomes.

AI is proving beneficial in drug discovery and identifying chemical markers within the body, equivalent to people who can signal the presence of cancer. They can control instruments utilized in robotic surgery. Additionally, AI technology just like the one behind ChatGPT can process medical literature and patient records to make complex diagnoses. You can automate writing tasks like note-taking and data entry.

Deep empathy

Integrating AI systems could help streamline operational tasks in healthcare services equivalent to the NHS. This includes bed management and hospital processes. However, the event of AI technologies mustn’t be haphazard, but have to be focused on real clinical needs and designed to advertise higher relationships between patients and staff. This is the pillar of deep medicine often known as deep empathy.

Healthcare has increasingly change into a discipline through which the human touch, once the cornerstone, is overshadowed by a relentless pursuit of efficiency. Health staff are facing one increased load of administrative tasks. This can lead to reducing the time they devote to every individual patient and negating the essence and potential advantages of compassionate care.

Staff need the sensitivity and time to answer the emotional and psychological needs of patients and their families. This promotes a supportive and compassionate care environment and strengthens the human connection at the center of healthcare.

AI solutions will be designed to cut back administrative burdens on staff and open up more opportunities for meaningful patient interactions. By removing these barriers, we will focus more on direct patient care, helping to enhance the standard of services provided and hopefully patient satisfaction.

There can be a transformative opportunity to reimagine efficiency and concentrate on relationships between patients and staff. It envisions a future through which healthcare professionals have each technical skills and emotional intelligence, and are in a position to meet the psychological needs of patients with real understanding and compassion.


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