In oncology, the usefulness of AI depends more on data quality and context than on data volume. While clinical trial data are standardized, everyday clinical data remain fragmented, making better data capture crucial. Advances like RNA sequencing expand possibilities, but turning data into trustworthy, actionable insights requires explainability.
AI adoption varies – radiology leads, while molecular pathology lags. Unlocking healthcare data’s full value will require rethinking patient consent, data infrastructure and reuse strategies. In practice, AI can streamline tumor boards, support evidence-based care and automate routine tasks. Yet challenges persist, including inconsistent data, regulatory hurdles, limited transparency and workforce gaps. Ultimately, AI’s promise lies in earlier detection, greater efficiency and improved patient outcomes through collaboration.
Why AI in cancer care depends on data quality
A common phrase in AI is that it needs “data, data, data.” But sheer volume is not the answer. What determines the usefulness of AI in oncology is not just how much data we have, but how it is generated, structured and applied. Even a small quality gap in the data – or missing data points – can have outsized effects on outcomes. Poorly contextualized or incomplete records can send algorithms down the wrong path, undermining the very purpose of using AI in patient care.
In practice, this means paying attention to the full process: how data is captured, the standards guiding its collection, and the purpose for which it was originally gathered. The raw quantity of information is far less important than its reliability and the integrity of the methods behind it.
Clinical oncology data challenges
In clinical trials, data quality is carefully managed through protocols. But in everyday patient care, data is often fragmented, inconsistently recorded, or scattered across systems that cannot easily communicate with one another. In such cases, any AI tool built on top of this foundation risks amplifying uncertainty rather than clarifying it.
The key lies in improving how data is captured in routine settings. Without this foundation, even the most sophisticated AI model cannot deliver meaningful or reproducible insights.
From complex data to actionable decisions
The tools available for generating biological data are advancing rapidly. RNA sequencing, single-cell analysis and other technologies can produce extraordinary details about a patient’s cancer. But the challenge remains: how to translate that complexity into decisions that matter for the individual. More data does not automatically mean better decisions – it needs to be contextualized, explained and linked to actionable options.
This is where explainability becomes central. If clinicians are to trust AI tools, they must not only deliver accurate outputs but also make it possible to understand why a particular conclusion or recommendation was reached. Transparent, explainable models build confidence and make adoption far more likely.
Adoption of AI in oncology: fast and slow lanes
The uptake of AI varies significantly across medical specialties. Radiology and radiotherapy are ahead of the curve, helped by standardized imaging formats and mature tools for automated image analysis. By contrast, fields such as molecular pathology are only beginning to experiment with AI-driven methods. This uneven progress reflects both technical readiness and the complexity of implementing solutions in different areas of cancer care.
Rather than expecting one “super model” to manage every dimension of oncology, the most realistic approach is a suite of specialized tools that serve particular functions –for example, tools for imaging, genomics or decision support – that can progressively be integrated into broader workflows.
Redesigning healthcare data collection for AI
To realize the full promise of AI, healthcare systems will need to rethink how data is collected and reused. Consent processes must allow patients to contribute their data not only for immediate treatment, but also for future research and secondary applications. Hospitals will need infrastructure that enables secure, interoperable and scalable data sharing, while ensuring compliance with ethical and regulatory frameworks.
Anonymization and de-identification techniques will play an important role, allowing valuable datasets to be repurposed without compromising patient privacy. By designing for reuse from the outset, healthcare systems can unlock far greater value from the data already being generated.
Practical opportunities for AI in cancer care
Beyond research, some of the most immediate opportunities for AI lie in easing the burden on clinical teams and supporting decisions. For example:
These kinds of use cases, focused on augmenting rather than replacing human expertise, are likely to gain traction first.
Barriers to adopting AI in healthcare
Despite the progress, several barriers continue to slow adoption. Data availability and quality remain uneven. Many models still lack sufficient performance for clinical use. And the “black box” nature of some AI systems raises concerns about explainability and accountability. On top of this, regulatory pathways are evolving, and healthcare providers face the challenge of investing in infrastructure and skilled personnel to implement solutions safely.
Overcoming these hurdles will require collaboration across startups, hospitals, regulators and pharmaceutical companies. Education will also be vital: clinicians must be trained not just in how to use AI tools, but in how to evaluate them critically and select the right tool for the right context.
The future of AI and healthtech in oncology
Ultimately, the promise of AI in oncology is not only about accelerating discovery, but also about transforming the patient’s journey.
AI points to a future where data works harder for patients, caregivers and clinicians alike. Realizing it will take persistence, trust and cross-sector collaboration – but the impact could be profound.
This article was inspired by discussions at DayOne’s Open Mic: Next in Health event on the topic “The role of healthtech and AI in cancer treatment” with speakers Manish Khatri, Novartis, Benjamin Kasenda, University Hospital Basel, Tim Heinemann, CSEM, and DayOne’s moderator Caoimhe Vallely-Gilroy.
Sign up to the DayOne newsletter to receive invites to future events about relevant industry topics.
