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Bertalan Meskó is the director of The Medical Futurist Institute, Budapest, Hungary, and is affiliated with the University of Debrecen, Debrecen, Hungary.
Tien Yin Wong is the vice-provost at Tsinghua University and founding director and chief scientist at the Beijing Visual Science and Translational Eye Research Institute, Tsinghua University, Beijing, China.
Every research programme is a bet on the future. Yet, scientists rarely examine the assumptions behind those bets. Decisions about grants, infrastructure, hiring, regulation and training often presume that certain technologies will mature, specific skills will be needed, the public will accept the resulting innovations and few risks will materialize. Yet these assumptions are rarely stated, let alone tested or revised in a systematic way1.
The consequence is a mismatch between current research activity and future conditions, a gap that will only grow with the pace of discovery and global change. If they don’t think ahead, scientific institutions will increasingly find themselves pressed to swiftly revise research priorities, training plans and infrastructure investments after a crisis emerges for which they were unprepared.
During the COVID-19 pandemic, for example, researchers had to accelerate vaccine development, scale up telemedicine, redeploy clinical staff, move education online and manage public trust in surveillance and vaccination. They had to do this under crisis conditions and with little initial knowledge of the virus2.
The rapid rise of artificial intelligence is also hard to navigate. Institutions must make decisions about validation, evaluation methods, workforce preparedness, research integrity and governance before long-term evidence is available3.
What is missing is not perfect prediction, but foresight: a structured, forward-looking process for examining multiple plausible futures, identifying the assumptions that matter across them, and defining signals that can trigger changes in research direction, staffing and funding priorities.
The field of ‘futures studies’ offers rigorous methods for analysing trends, planning for a range of outcomes and using ‘horizon scanning’ to scope out future risks and opportunities. Yet these methods aren’t mainstream in scientific practice. It’s time for that to change.
Here, we call for futures methods to be plugged into the core mechanisms of scientific development, to inform decisions about what science should fund, build, teach, validate and evaluate. We call this concept translational foresight. Adopting it would make the promises embedded in research programmes explicit, testable, traceable and revisable (see ‘Translational foresight’).
Looking ahead
Translational foresight has parallels with translational medicine. In the late twentieth century, molecular biology was advancing rapidly, but that knowledge was not reaching the clinic4. Expressions in medical research such as “bench to bedside” and “crossing the valley of death” highlighted this gap5. In response, scientists reoriented their systems to link discovery to patient outcomes6. This was accompanied by the rise of clinician-scientist training programmes7, changes in the funding model for translational research and the development of dedicated institutions for translational medicine8.
Similarly, the futures-studies field has established methods for exploring future trends, uncertainties and alternative trajectories. These methods are not routinely used in science-research contexts.
The Futures Wheel is a method for graphically visualizing direct and indirect consequences (see ‘Futures-thinking tools’). It was used by some governments to map cascading impacts of the COVID-19 pandemic, revealing shifts towards telemedicine, workforce strain and changes in care delivery, for instance9.
Futures-thinking tools
Exercises to explore future research directions and anticipate problems.
Method
Purpose
How to Run It (60–90-minute version)
Output
When to Use
The Futures Wheel
Explore consequences of a trend or milestone
State a central change, such as “AI can now co-author scientific papers”; list direct and indirect effects; highlight unintended consequences.
Map of risks, opportunities and ripple effects
Before adopting a technology, submitting a grant or implementing a policy
2×2 Scenario Analysis (One Driver, One Uncertainty)
Structure uncertainty into four plausible futures
Choose one key driver, such as AI growth, and one uncertainty, such as regulation; combine them into four scenarios; ask which strategies work across all four.
Four distinct future worlds; list of robust versus fragile strategies
Strategic planning, long-term research direction, infrastructure investment
Vision Writing (Backcasting Narrative)
Define the desired long-term impact
Choose a time horizon, such as 2035; write from the perspective of a future researcher or clinician; identify what changed, what worked and what failed.
Clarify long-term aspirations and milestones
Setting a laboratory mission, forming an institution
Stump the Futurist (Research Version)
Stress-test assumptions and blind spots
Propose a future claim; challenge it with failure modes and missing variables; revise the claim with explicit conditions.
Refined assumptions and conditional forecasts
Grant preparation, technology assessment, proposing controversial hypotheses
Scenario analysis is another technique that has informed long-term strategy in industry and policy. For example, energy company Shell has utilized it to respond to energy crises, and governments have used it to develop national policies on climate, infrastructure and mobility.
Forecasting platforms, such as the Good Judgment Project, which is led by researchers at the University of Pennsylvania in Philadelphia, harness the wisdom of the crowd to answer forward-looking questions across the political, economic and social spectrum.
Without such tools, science relies on informal judgment, intuition and short-term planning to anticipate its future implications.
Pandemic-preparedness frameworks, for example, can identify biological and logistical risks, such as whether health systems have enough tests, vaccines, beds, protective equipment and emergency plans. But they rarely shape research priorities, workforce planning or technology validation needs before a crisis begins10.
Translational foresight turns preparedness from a static emergency checklist into a system for testing and updating the assumptions on which future decisions depend. For example, funders could support the testing of remote care, home monitoring and rapid-diagnostics systems before they are urgently needed. Health systems could define trigger points in advance — from occupancy of intensive-care units to levels of public distrust — that would prompt revision of research, staffing or funding targets.
Similarly, personalized and genomics-based vaccine research involves not only designing vaccines, but also making assumptions about the future world in which they will be used11. Scientists and funders implicitly assume who the target populations will be, how microbes will evolve, what genomic data will be available, whether regulators will accept personalized vaccine strategies and whether the public will trust the vaccines. Recognizing these assumptions at the start will enable projects that can adapt if conditions change.
The challenge lies in translating the outputs of futures methodologies into research priorities, funding decisions, infrastructure planning and evaluation. Several barriers have kept this translation from becoming routine.
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