4 minutes to read With insights from... David Elliman Global Chief of Software Engineering david.elliman@zuhlke.com When it comes to AI in healthcare, the gap between promise and reality is hard to ignore. While headlines are filled with stories of revolutionary AI tools transforming diagnostics, care, and drug discovery, the practical reality for many in the health sector is far less impressive. Prototypes and pilots often generate excitement, but few have delivered scalable, real-world results. The truth is that AI has immense potential to revolutionise healthcare, but its full impact remains just out of reach. So, how do we cut through the hype and get to the real, tangible outcomes? The potential for health outcomes seems endless. 24/7 access to centuries of medical knowledge; instant insights that fact-check against every biometric dataset ever recorded; and drug discovery programs that can run millions of trials at once. But where does AI’s healthcare promise overlap with its real-world potential? Incremental adoption: a holistic analysis of AI’s future in healthcare Read the full report With the help of input from industry leaders, healthcare practitioners, and AI experts, Zühlke’s new whitepaper – Incremental adoption: a holistic analysis of AI’s future in healthcare – seeks to answer exactly that question. Here’s a sneak peek at what we’ve learned… A state of cautious optimism Technology is a polarising topic in medicine, since those who work across pharma, MedTech, and healthcare often have wildly different experiences with it. Where on the one hand you already have teams using machine learning algorithms to trial new drugs, or using federated learning to spot patterns in radiology results, on the other you’ll find teams lumbered with outdated, legacy computer systems that feel a world away from anything ChatGPT has to offer. 'We hear a few interesting examples of new technology', said one of our research interviewees, 'but it feels a long way away from the IT we have now'. Another described the industry as being right 'at the top of the [AI] hype cycle'. That said, there is some guarded optimism about its many possibilities, in particular with new technologies like GenAI. Another interviewee told us that 'while GenAI doesn't currently have any formal use, there is lots of interest at the research level'. AI in medicine is making inroads, then, and its ability to see every inch of massive datasets at once makes it a good fit for a wide range of health-based use cases. Examples of AI in healthcare Clinical decision support and diagnostics Clinical decision support and diagnostics AI systems can analyse complex datasets (like medical imaging and patient records) and provide diagnostic insights. Personalised care Personalised care Tools like AI-driven chatbots and virtual health assistants are being integrated into healthcare systems to provide patients with 24/7 access to health information. Automated administration Automated administration Automating tasks like scheduling, billing, and patient record management could reduce the burden on healthcare staff. Drug discovery Drug discovery AI technologies are being used to accelerate the identification of potential drug candidates by analysing large chemical compounds and biological interaction datasets. Mental health Mental health AI-driven applications are being developed for mental health to detect early signs of mental health conditions and provide therapeutic care. Chronic disease management Chronic disease management Monitoring patient health data remotely allows for timely interventions. This is particularly valuable for conditions like diabetes, hypertension, and heart disease. Public health management Public health management By analysing trends and patterns in large datasets, AI tools can help public health officials predict and manage outbreaks, as well as optimise vaccination processes. There may be trouble ahead… This could all be game-changing stuff, but there’s no such thing as a free lunch. For all of AI’s strengths, there are an equal – if not greater – number of stumbling blocks. To that end, a few common concerns emerged from our research, each presenting serious challenges to AI deployment across the full gamut of medical industries. We can broadly group these challenges into four buckets: Ethical and security concerns Algorithmic biases Human adaptation Integration complexity These challenges are big and multifaceted, but they’re not insurmountable – they need careful consideration and the right processes in place to overcome them. 'What's the smallest use case that has a testable answer?' asked one large healthcare company’s Data Science Director. It’s a back-to-basics ethos that speaks to a universal truth about the implementation of AI for healthcare: the need to start small and grow from a point of proven success. 'There may be a big addressable market', our interviewee added, 'but starting with proof of functionality is crucial'. But there are other ways around the challenges at hand – and other methodologies for ensuring sustainable data and human oversight. In our full report, Incremental adoption: a holistic analysis of AI’s future in healthcare, we’ve turned over every rock in the AI healthcare ecosystem to determine how to mitigate the technology’s most glaring issues, how to measure its impact, and – crucially – how to navigate the steps between now and what’s next. To understand where the AI healthcare hype stops and its real-world adoption can start, get the full report: AI in Healthcare: Transforming Potential into Reality AI has the power to revolutionise healthcare, but how can organisations navigate its complexities and unlock true value? Discover how to unlock AI’s full potential for healthcare. AI in healthcare: the hype-free prognosis Overcoming key challenges in AI for healthcare
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