Artificial intelligence is revolutionising industries across the globe, and healthcare is no exception.
From diagnostics and drug discovery to data analysis and patient monitoring and much more, AI is transforming how we approach healthcare.
Health Tech World speaks to experts in the field to discover their thoughts on AI’s future in the sector, where it will have the most impact and what challenges and opportunities we may expect to see.
Chris Meier, Managing Director and Partner at Boston Consulting Group believes that AI is reshaping healthcare across the board – from accelerating R&D and refining diagnostics to care delivery and precision medicine – the adoption of AI has progressed significantly in the last five years.
“AI tools today are being used to discover novel medicines, analyse data from wearable devices for more accurate diagnosis, and support physicians and hospitals in providing highly personalised care to patients,” said Meier.
“AI technology is also revolutionizing how we discover, diagnose, and deliver treatments. As a result, it is making healthcare more efficient and helping deliver better health outcomes for patients and physicians.
“In the last few years, many healthcare organisations have started exploring AI and Generative AI and learning how to apply it. Established players are well-positioned to benefit from AI due to their deep expertise and understanding of the space.
“Many of them also have access to extensive data – a key ingredient for the successful application of AI. In contrast, new entrants, often called ‘tech challengers’, can be more agile and innovative, which enables them to reshape healthcare at a deeper level.
“Looking ahead, we expect organisations to focus on implementing and scaling AI, to fully realise its potential. This includes changing business processes to fully embed the use of AI tools and upskilling the workforce.”
Yannik Schrade, CEO and co-founder of private supercomputer, Arcium, said: “Improving the healthcare system should not come at the cost of patient privacy, but that’s what’s at stake with the integration of Artificial General Intelligence (AGI) and machine learning.
“Training and operating these machines requires the processing of vast swaths of patient data, which is then exposed to third parties and potentially even cyberattacks. While healthcare data centers have long been the target of malicious actors, AGI increases the scale and costs of these incidents.
“The good news is that private computing platforms like Arcium can be used to mitigate the risks associated with AI so that healthcare and other sectors can leverage this technology without privacy or security concerns.
“With Arcium’s infrastructure, data can be processed in an entirely encrypted state, so that a program can analyse and report critical information without exposing underlying patient information. So innovation does not have to come at a cost to individual privacy.”
Trond Aas, CEO at Attensi, said: “The future of AI powered training in healthcare is incredibly promising – it is one of the most exciting eras of innovation that we have witnessed and it has significant impact for healthcare staff and their patients.
“The high quality of the training experiences we can now produce, including hyper-realistic simulations and conversational role-play training with virtual humans, were simply not achievable before recent advances in AI. Furthermore, thanks to AI, the impact of that high quality level of training is only enhanced by the speed and volume at which we can create it.
“We are now helping healthcare settings to create training content in-house with an AI powered platform that is supported by extremely advanced AI co-pilots and AI assistants. High quality training content at scale and speed without the need to code.
“AI will continue to enhance the efficiency, effectiveness, and personalisation of training programmes, leading to better-prepared healthcare professionals and improved patient outcomes.”
Aas believes that, in training, AI will be most impactful in soft skills training and education, content creation, and data analysis, helping leadership teams to assess the quality and success of training, identify gaps and areas to improve and inform decision-making processes for future training.
Aas highlights this will not be without its challenges, noting that integration with existing systems will be a hurdle to overcome to ensure seamless integration in order to maximize the benefits of AI in healthcare training.
However, with these challenges also come opportunities, as Aas explained, including personalisation, as AI can help tailor training programmes to individual learning styles and needs; scalability, as AI-powered tools can rapidly scale training programmes with consistently high quality to accommodate large numbers of healthcare professionals; continuous improvement, as AI systems can continuously learn and improve based on user feedback and performance data.
Aas says areas for improvement with AI include enhanced realism, as further advancements in AI and simulation technology can make training scenarios even more realistic, providing healthcare professionals with a more immersive and authentic learning experience; and in accessibility, ensuring that AI-powered training tools are accessible to all healthcare professionals, regardless of their technical expertise or resources.
Aas said: “In summary, the future of AI in healthcare training is bright, with numerous opportunities to enhance the efficiency, effectiveness, and personalisation of training programs.
“By addressing the challenges and leveraging the opportunities, AI can significantly improve the preparedness of healthcare professionals and ultimately lead to better patient outcomes.”
Christian Hardahl, EMEA Healthcare Leader at SAS, said: “The future of AI and machine learning in healthcare promises a profound shift in how care is delivered, making it more precise, efficient, and responsive. These technologies are already driving change, with innovations like computer vision and natural language processing (NLP) enabling earlier and more accurate diagnoses, as well as advancing personalised and precision medicine.
“Meanwhile, predictive analytics is helping clinicians make informed decisions, and AI-driven solutions are enhancing treatment precision and optimising hospital operations. A major challenge facing healthcare today is the backlog of patients, with NHS waiting lists reaching 7.5 million. AI-powered analytics, alongside AI solutions like Patient No-Show and Surgical Risk Assessments, can help tackle this issue by modelling patient journeys, predicting bottlenecks, and optimising resource allocation across hospitals and social care.
“For example, our partnership with the Maxwell Centre at the University of Cambridge is embedding the SAS Viya platform into academic research and health-tech innovation. This collaboration enables targeted research on high-impact projects, supporting both academia and promising early-stage startups, and a standout successful project of this partnership has been using AI-powered analytics to improve kidney transplant qualification. This has the potential to make more kidney organs available for transplants which in turn will save lives, enhance the quality of life for more than 100 people each year in the UK alone, and significantly reduce NHS costs.
“However, the effectiveness and widespread deployment of AI in healthcare, particularly within the NHS, is hampered by challenges related to data literacy and the development of the necessary skills. Healthcare professionals, from clinicians to administrators, often lack the technical expertise to fully interpret and leverage the vast amounts of data generated by AI systems. Without a foundational understanding of how AI and machine learning algorithms work, there can be scepticism, misinterpretation, or underutilisation of these powerful tools.
“Ensuring that healthcare staff are adequately trained in data literacy is crucial to unlocking AI’s full potential. This includes not only understanding the algorithms themselves, but also developing the capability to critically assess the results and integrate them into decision-making processes. Addressing this skills gap will be pivotal in overcoming barriers to AI’s adoption and maximising its ability to drive improvements in patient care, operational efficiency, and overall healthcare outcomes.
“The SAS Viya platform facilitates this, by providing a programming interface for both technical users, like data scientists, and non-technical users, such as researchers and clinicians. This eliminates programming barriers for non-technical users, allowing them to focus on understanding the AI techniques applied to their health data without worrying about coding syntax or requirements.
“By leveraging AI to enhance diagnostics, predict patient outcomes and streamline healthcare operations, clinicians can gain powerful data-driven insights that enable better, more personalised care for their patients. AI also plays a key role in improving hospital efficiency, from optimising staffing resources to reducing readmissions and ensuring that patient flow is managed effectively. AI-driven models can integrate data from bed availability, hold times and social care resources to enable more efficient discharge planning and prevent avoidable delays.
“Going forward, as healthcare organisations face increasing pressures to improve outcomes while controlling costs, the integration of AI will be essential to ensuring more effective, efficient and accessible care.
“With continued advancements, AI will play an even greater role in shaping the future of healthcare – supporting early interventions, streamlining patient journeys and ensuring that healthcare systems can operate at peak performance.”
Steve Herne, CEO at Unlearn, believes that AI can transform clinical research.
Herne said: “Clinical research is a slow and expensive – late stage trials take years and cost between US$30m to 50m on average in the U.S., all while patients wait for life-saving therapies.
“The future of healthcare depends on how well the industry integrates AI and machine learning to conduct more efficient studies.
“One breakthrough example is AI-powered digital twin models that predict how an individual patient’s health would progress with or without the experimental drug. Digital twins of clinical trial patients enable trials to be run with smaller sample sizes, accelerating clinical development timelines and getting new treatments to patients sooner.”
Sam Dorison, CEO of ReflexAI, said: “As the healthcare industry evolves, organisations are realising that a strong reputation isn’t just about visibility and medical expertise – it’s about delivering a consistent, positive experience for patients at every touchpoint. This evolution has led many healthcare organisations to focus on improving patient interactions and building long-term trust.
“A positive patient experience leads to stronger relationships over time. For example, someone who has a positive urgent care visit is more likely to return to the same provider when they are seeking a specialist in the future.
“AI and machine learning are playing a key role in making this happen. Technologies help healthcare teams communicate more clearly, respond with empathy, and improve overall efficiency. AI-powered training tools enable staff to refine their skills in handling everything from routine inquiries to complex, sensitive conversations.
“The focus is not only on clinicians but also on patient support teams that manage inquiries about treatment options, medication side effects, and billing. In a resource-constrained world, using these technologies can reduce manual training costs by over 50 per cent while leading to double-digit improvements in staff skills
“As AI continues to evolve, its role in shaping healthcare experiences will only grow. Organisations that focus on both technology and patient-centred care will be best positioned to build lasting relationships and improve healthcare delivery for the long term.
“They will also be the ones that can innovate more quickly due to their stronger foundations, leading to better patient outcomes and financial metrics.”
