What is the future of technology in health data?
From robot-assisted surgery to personalised disease treatment - technology can do some truly remarkable things. But while the healthcare industry has been using artificial intelligence (AI) and machine learning (ML) since the 70s, only in recent years have we witnessed a revolutionary leap in their application.
Just 10 years ago, AI was capable of handling narrow, predefined tasks like recognising handwritten digits or identifying objects in images. These systems often relied on manual coding and lacked the depth to understand the context or nuance behind medical data. But when you compare that to today’s models, AI is no longer a one-track mind. It’s more dynamic and adaptable, being bolstered by algorithms capable of analysing huge swathes of data, at a scale that was previously deemed impossible.
The pace of innovation has been extraordinary. But it begs the question: What do the next 10 years have in store for the healthcare industry? And, what more should we be doing now to bring effective, patient-focused care to the forefront?
The promise of personalised medicine and genomics
One of the most promising frontiers in terms of AI having a positive real-world impact is personalised medicine. Through AI-powered analytics, clinicians have been able to consider myriad variables, like genetic predispositions and lifestyle factors, to tailor healthcare interventions to individual patient profiles. A significant shift away from the traditional ‘one-size-fits-all approach’.
To take things a step further, the integration of AI with genomics has also been transformative. As the cost of genome sequencing plummets, AI systems are analysing complex genetic data to forecast disease risk and personalise treatments at a molecular level. This means that, for many conditions, we can intervene before symptoms even appear. For instance, research published in Nature conducted by our parent business, Optima Partners, along with Biogen and the University of Edinburgh, revealed they could accurately predict a person’s risk of disease before symptoms even appear.
This level of granularity helps in forecasting disease risk and designing targeted treatment plans – helping the industry move from reactive care to proactive healthcare.
A glimpse into the future of healthcare
Looking ahead, the next decade promises even greater advances and I have no doubt we’ll see a lot more integration of AI in clinical workflows.
Cross-border collaboration is another area with significant potential. Recent global health crises, like COVID, underpinned the importance of international cooperation, and with continued advancements in technology, it’s becoming more common. In fact, the EU is already planning to develop a European Health Data Space to further encourage collaboration across countries.
By harnessing collective intelligence from around the world, AI not only improves how we collect, analyse, and interpret health data to detect and prevent potential outbreaks. It also accelerates the development of treatments and vaccines. This kind of global interoperability signals a future where healthcare becomes a shared responsibility, backed by real-time insights.
However, as the volume and sensitivity of health data grow, so does the need for robust data privacy and governance frameworks. To ensure ethical use and public trust, new regulations and technologies are emerging that enable AI to operate within privacy-preserving architectures.
Systems like this will allow AI to learn from patient data without ever exposing the underlying information – striking a critical balance between innovation and confidentiality. Transparent consent mechanisms, clear accountability, and strong cybersecurity measures are becoming the foundation upon which the future of the healthcare industry will be built. Without this trust, we’ll struggle to reap the benefits of AI.
Of course, in order for all these technological possibilities to reach their full potential, there must be an emphasis on accessibility, collaboration and, most importantly, equality. Technology must serve as an enabler for all – regardless of geography, socioeconomic status, or medical history.
To truly harness the power of health data, stakeholders across the entire healthcare ecosystem – including government, industry and academia – must work in tandem.
No matter how advanced the tech, its impact depends on people who know how to use it. That includes training doctors, clinicians, administrators, and health policymakers to confidently interpret AI outputs and know when to trust or challenge them. We’re entering an age where data literacy will be a non-negotiable: the sooner we prepare, the better care we can deliver.
As healthcare becomes increasingly automated and algorithm-driven, it's critical that ethics and equity aren’t afterthoughts; they must be embedded from the ground up. That means ensuring datasets are inclusive of all patients to accurately and effectively treat them. The problem is, data isn’t always unbiased, so regularly monitoring and auditing algorithms will help avoid perpetuating biases.
Beyond fairness, there’s also the ethical stewardship of emerging technologies; like ensuring patient consent is clear, data use is accountable, and that AI aids human judgment, rather than replacing it.
By weaving inclusivity into every stage – from design to deployment – we don’t just build smarter systems; we build healthcare that works for everyone.
The future of health data is not just about machines or numbers, it’s about people. We’re already seeing huge leaps in personalised medicine and genomics to ensure a people-first approach to health. But to truly harness the power of AI, the entire healthcare ecosystem, from governments to clinicians, must be invested to reap the benefits for decades. By placing societal wellbeing at the centre of innovation, we can ensure that the next wave of health technologies doesn't just deliver better data, but better lives.
About the author
Dr Zhana Kuncheva is the director of health data sciences at bioXcelerate. Kuncheva received a degree in Mathematics, Operational Research, Statistics and Economics from the University of Warwick with a Master specialisation in probability theory and time series analysis (2012). She also received a PhD in Statistics from Imperial College London (2017) with a focus on complex network analysis. Kuncheva spent four years at C4X Discovery – a small molecule development biotech – as a genetics data science lead where she was running the R&D development of the proprietary software Taxonomy3 for genetic data analysis and population stratification. She also spent close to two years at Silence Therapeutics – a silencing RNA technology company – as a senior scientist in translational genomics. Kuncheva joined Optima Partners, the parent company of bioXcelerate, in May 2022 and has since established a small team of highly qualified scientists in early phase drug discovery. Beyond causal inference tools, Zhana holds interest for unsupervised machine learning approaches for ranking and clustering.
