Precision medicine—often termed precision oncology when applied to cancer care—aims to deliver tailored prevention, detection, and treatment by using patient-specific information. The authors position genomic profiling as increasingly used for diagnosis and therapy adaptation across several tumor types. They note that artificial intelligence (AI) can support precision oncology by analysing large volumes of relevant clinical, genomic, and other health data, but emphasise that the translation of these technologies into routine care requires foundational resources such as high-quality data and sustained financial investment in the health system.
This article reports qualitative findings drawn from a series of expert workshops that built on quantitative outcomes from a prior foresight exercise (reported elsewhere). The workshops were convened to gain more detailed insights into the prospective development of precision oncology in Belgium and to discuss the role of AI in that future. The qualitative approach focused on scenario building: participants explored four hypothetical future scenarios centred on technological and economic issues that could influence widespread adoption.
The authors highlight the growing use of genomic profiling for cancer diagnosis and therapy adaptation. They emphasise that the clinical value of precision oncology depends on access to large, high-quality datasets that allow reliable interpretation of genomic and other molecular results. The report underscores that data quality, interoperability, and availability are key enablers for implementing precision medicine at scale.
AI is presented as a supportive technology capable of processing vast and complex datasets relevant to precision oncology. The article describes AI's potential to assist in diagnosis, risk stratification, and treatment selection by identifying patterns across genomic, clinical, and population-level data. However, the authors make clear that AI tools alone are insufficient without appropriate data infrastructure, governance, and investment to support their safe and equitable deployment.
The expert workshops examined four hypothetical scenarios focusing on technological and economic challenges to the broad use of precision oncology. The source reports that these scenarios were used to probe feasible implementation approaches and to identify policy responses. Specific scenario content and detailed scenario outcomes were not reported in the abstract; the workshops collectively demonstrated that each scenario would require policy action to realise benefits for patients and the health system.
A central finding of the workshops is that policy responses must extend beyond addressing technological hurdles and financing. The authors argue for comprehensive policy design that incorporates social and governance dimensions. Examples cited in the study include the need for participatory approaches to policy-making and the creation of institutional structures to coordinate precision medicine efforts across disciplines and sectors.
Workshop participants recommended involving patient associations and the general public in the design of policies for precision oncology and AI. The article also supports establishing multi-disciplinary expert groups to guide implementation, reflecting the view that clinical, ethical, legal, and societal perspectives are necessary components of successful adoption. These stakeholder-engagement measures are presented as complements to technological solutions and funding strategies.
The authors state that, to their knowledge, this is the first study to apply foresight methodology specifically to illustrate possible futures for precision oncology in Belgium while explicitly examining the role of AI. The work aims to broaden discussion from purely technical and economic considerations to include governance, public engagement, and cross-disciplinary coordination. Limitations reported in the abstract include that the qualitative article builds on a prior quantitative exercise; detailed scenario descriptions and workshop outputs beyond the high-level conclusions are not included in the abstract.
The study concludes that the four futures explored would all benefit from supportive policy measures in Belgium. Effective implementation of precision oncology and AI in routine cancer care will require more than technological readiness and funding: it will also depend on data quality and governance, stakeholder engagement (including patient groups and the public), and the formation of multi-disciplinary expert bodies to inform policy and practice. These conclusions are offered to inform policymakers and health-system stakeholders considering how to prepare for and shape the integration of precision medicine and AI into cancer care in Belgium.