How will generative AI impact healthcare?

Amid the constant complexities that challenge healthcare, generative AI shines as a beacon of hope. With a market opportunity worth a colossal $6 trillion, according to Morgan Stanley, it's impossible to ignore the impact that this technology is set to have. Generative AI isn’t just a passing trend; it's a rapidly evolving ecosystem of tools growing in popularity showing huge potential to revolutionise healthcare in ways we've never seen before. This technology encompasses much more than just ChatGPT, and can handle various data types including text, images, audio, video, 3D modelling, and even coding. While some estimates suggest that it could even raise global GDP by 7% over 10 years, the potential applications of generative AI go well beyond just economic gains.

Our world is grappling with many challenges to global health. With an increasing incidence of pandemics, a surge in chronic diseases, mental health concerns and a rapidly ageing population, the healthcare industry is undoubtedly struggling to keep up with the rising demand for high-quality healthcare services.

According to the World Health Organization (WHO), there is a 1 in a million chance of a person being injured while travelling by aircraft. In comparison, there is a 1 in 300 chance of an individual being harmed throughout the patient journey. Prior research has shown that up to 50% of all medical errors in primary care are due to administrative reasons. The global shortage of medical professionals further compounds these problems. The WHO also estimates a projected shortfall of 10 million health workers by 2030, mostly in low- and lower-middle-income countries making it increasingly challenging to provide care to everyone in need.

Whilst generative AI is poised as a potential solution to tackle some of these challenges, it's important to note that this technology is still evolving and comes with its own set of challenges. The output accuracy of generative AI is highly dependent on the quality of the datasets used to train them, including medical records, lab results and imaging studies. Any error in the AI-generated treatment and management plan could potentially put the patient's health at risk, making it imperative that healthcare providers and patients have complete trust in the technology. Another concern is the possibility of bias in the algorithms used by AI systems. If an algorithm is trained on a dataset that is not fully representative of the population, it can produce results that are not accurate or even harmful. Therefore, it is essential to address these challenges to ensure the technology's ethical use, improve healthcare outcomes, and ultimately benefit patients.

1) Clinical Decision Making

Generative AI is already assisting doctors and medical professionals in making accurate and informed diagnoses. Generative AI can analyse data from a patient's medical records, lab results, previous treatments and medical imaging, such as MRIs and X-rays, to identify potential problem areas and suggest further testing or treatment options. One example proving this ability is Glass.Health who have created a generative AI tool capable of generating diagnoses and clinical plans based on the input of symptoms.

2) Risk Prediction of Pandemic Preparedness

According to National Geographic, there are more viruses in existence than stars in the universe, and on average, approximately two new species of human viruses emerge every year. As we have seen with the recent global pandemic, a new human-to-human virus without prior immunity could rapidly escalate into a pandemic, leading to millions of fatalities. Generative AI models have emerged as a vital source of insights for scientists studying the societal-scale effects of catastrophic events, such as modelling new pandemics and developing preventive measures. For instance, new generative AI models are being trained on large amounts of protein sequences to identify new antibodies which could address infectious diseases and support outbreak response.

3) Personalised medication and care

The ability to provide personalised care is essential in today's healthcare landscape. Wearable devices can collect real-time, continuous data on a patient's health indicators, including heart rate variability, blood oxygen and blood glucose levels. The data can then be fed into generative AI algorithms, which can analyse and interpret the data and offer tailored recommendations and treatment options. In this way, AI would be deployed to manage diseases detected by wearables, like cardiovascular disease, for example. By leveraging wearables and at-home monitoring devices with generative AI, healthcare providers can move away from the traditional, reactive healthcare model to a proactive one.

4) Improved drug discovery and development

Generative AI has shown promising results in drug discovery and development. One notable example is Insilico Medicine, a company that has developed a generative AI platform called GENTRL which has been used to design new drugs for diseases such as fibrosis and cancer. The results of these are astounding and mark a significant shift for the industry, which has conventionally relied on old-fashioned methods such as manual patient diary entries, faxed records and snail-mailed findings to regulatory agencies.

While the emergence of generative AI is exciting for many in the healthcare industry, it's natural for others to feel nervous and uncertain of its future. Nevertheless, the potential for revolutionary progress within healthcare is undeniable. As such, the choices made by healthcare providers, practitioners, policymakers and other stakeholders in the coming years will be critical in shaping the evolution of this technology.

As with any innovation, we must approach enerative AI with caution, acknowledging that its impact could be transformative, provided we adapt to its unique challenges and opportunities. Moments like this don’t come around often. A future marked by generative AI technology will usher healthcare into a new era of innovation and those daring to experiment and lead in this space will help create opportunities for patients, providers and healthcare institutions alike.