The Future Of Medicine Is Becoming A Science: 14 Studies That Show Why

For years, I have been working towards a personal goal: establishing Medical Futures Studies as a new scientific field. We regularly hear predictions about the future of healthcare, from artificial intelligence replacing certain medical tasks to the transformation of hospitals and medical education. But how often are these predictions based on established scientific methods?

Futures studies has existed as an academic discipline for decades, with more than 50 methods for systematically exploring possible futures. Yet, when we conducted our scoping review of healthcare foresight, we found only 59 relevant studies, using just eight of these methods. This enormous gap convinced me that medicine needed its own dedicated scientific subfield for studying the future.

Together with Tamás Kristóf, we launched the first theme issue dedicated to Medical Futures Studies in the Journal of Medical Internet Research. I consider this one of the most important steps we have taken at The Medical Futurist Institute. A new scientific field needs a research community, peer-reviewed publications, methodological standards and real-world applications. Our theme issue, which now contains 14 papers published between 2024 and 2026, is a major first step towards that goal.

We also recently published a Comment in Nature, “The science of foresight: how to future-proof your research,” arguing that futures methods should become part of how scientific research is planned, conducted and evaluated. The idea is straightforward: scientific decisions are often based on assumptions about future developments, and those assumptions deserve systematic examination.

Here are the 14 papers from our JMIR theme issue, the futures methodologies they used and the questions they explored.

The 14 Studies In Medical Futures Studies

1. Exploring the Need for Medical Futures Studies: Insights From a Scoping Review of Health Care Foresight

Method: Scoping review of futures methodologies. Our team analyzed 59 published studies to understand how established futures methods have been applied in healthcare. We identified their applications in national healthcare strategies, policymaking, pandemic preparedness and medical specialties, while documenting the methodological gaps that motivated this new field.

2. Technology Integration to Support Nurses in an “Inpatient Room of the Future”: Qualitative Analysis

Methods: Horizon scanning, scenario analysis, technology assessment and backcasting. Researchers explored how future hospital rooms could better support nurses through integrated technologies, automated monitoring and improved workflows. They involved 26 nursing professionals to identify practical needs and opportunities.

3. A Practical Guide to Using Futures Methods in Health Care: Approaches, Applications, and Case Studies

Methods: Scenario analysis, horizon scanning and the Futures Wheel. Our team developed a practical methodological guide demonstrating how healthcare professionals, researchers and policymakers can use established foresight techniques to explore uncertainties and prepare for different futures.

4. Participatory Design Approach in the Use of Scenario Analysis for Futureproofing Medical Education: Case Study

Method: Scenario analysis combined with participatory design. Researchers at Trinity College Dublin explored four possible futures shaped by digital connectivity and climate change. They examined which competencies, mindsets and curriculum changes future physicians might need by 2035.

5. Decentralized Biobanking Pathway to Precision Medicine: Futures Study

Methods: Backcasting and the Futures Wheel. The researchers explored how decentralized biobanking could give patients greater involvement in managing their biological samples and research data. They combined futures workshops and participatory research with a real-world breast cancer biobanking pilot.

6. Framing the Convergence of One Health and Digital Health in the Global South With a Gender-Sensitive Foresight Perspective: Delphi Study Using Latent Semantic Analysis

Methods: Delphi study, latent semantic analysis and foresight signal classification. Using responses from 45 experts across 19 countries, the researchers examined how digital health and One Health could converge in the Global South, identifying emerging trends, weak signals and differences in perspectives by gender.

7. Reporting Horizon Scanning Studies: Prototype Development Study

Method: Horizon-scanning methodology development. Researchers developed a 35-item reporting checklist to improve how horizon-scanning studies are documented. This is especially relevant for establishing Medical Futures Studies because transparent and reproducible research methods are essential to scientific credibility.

8. Backcasting the Trust Gap: A Strategic Road Map for Clinician Adoption of AI Diagnostics by 2040

Method: Backcasting. Starting from a desired future in 2040, the author examined what changes would be necessary to establish sustained clinician trust in AI-supported diagnostics. The analysis addresses the gap between improving technological performance and actual clinical adoption.

9. Horizon Scanning for Medical Technologies: Methodological Framework Development Study of the Medical Innovation Scanning Techniques (MIST) Framework

Methods: Horizon scanning. The researchers developed the MIST framework for systematically identifying and evaluating emerging medical technologies at different stages of maturity, integrating technological readiness, regulatory considerations and uncertainty.

10. Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study

Methods: Scenario planning and discrete-event simulation. The study modeled four possible futures for AI automation and data interoperability in oncology. It explored how different implementation strategies could influence treatment delays, patient throughput and operational bottlenecks.

11. Mapping Practice-Based Signals of Generative AI in Psychiatric Care: Qualitative Study of Korean Psychiatrists’ Experiences, Interpretations, and Implementation Priorities

Methods: Qualitative thematic analysis informed by horizon-scanning concepts. Researchers analyzed responses from 311 South Korean psychiatrists to identify early signals of how generative AI is changing psychiatric practice, including patient interactions, clinical risks and implementation priorities.

12. Forecasting the Impacts of Artificial Intelligence Assistance in Virtual Consultations for Chronic Obstructive Pulmonary Disease: Exploratory Futures Wheel Study

Method: Futures Wheel. Our study brought patients with chronic obstructive pulmonary disease and healthcare professionals together to explore the potential consequences of AI-supported virtual consultations. The analysis covered eight areas, including clinical relationships, psychological effects, ethics and healthcare delivery.

13. A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory Challenges

Methods: Three Horizons model, scenario planning and causal layered analysis. The authors proposed a framework for anticipating future governance challenges as clinical AI systems become increasingly autonomous. It addresses emerging risks, shifting professional responsibilities and the evolution of regulatory requirements.

14. The Digital Evolution of the Medical Black Bag: Environmental Scan With Trend Analysis and Horizon Scanning

Methods: Trend analysis and horizon scanning. Our team explored how the traditional medical black bag could evolve with portable diagnostic technologies. We systematically examined current instruments, commercially available digital tools and emerging technologies that might become part of a physician’s diagnostic toolkit over the next decade.

What Comes Next For Medical Futures Studies?

What I find particularly encouraging is the diversity of these studies. They address AI, medical education, nursing, oncology, diagnostics, biobanking, regulation and public health. They also demonstrate that futures methods can be used in very different research settings.

Our goal is to make these methods accessible to anyone interested in systematically examining the future of medicine, including clinicians, researchers, policymakers and patients.

We now have a growing collection of peer-reviewed studies, practical methodological guidance and examples of real-world applications. The next steps should include more researchers applying these methods, stronger methodological standards and the integration of futures studies into medical education.

I hope that one day, conducting a rigorous analysis of the future of a medical specialty will be as natural as conducting a systematic review of its existing evidence.

That is the future I am working towards.