The Need for Synergy in Foresight Research for Healthcare and Medical Sciences

Abstract

The rapid integration of artificial intelligence (AI) and machine learning (ML) technologies into healthcare represents a transformative opportunity. These technologies can interrogate and interpret complex medical datasets, thereby offering the potential for enhanced diagnostics, better patient outcomes, and optimized resource allocation.[1,2] We propose combining AI and ML with foresight methods, in synergy with medical expertise, and respecting the principles of responsible research and innovation (RRI) to guide research and support decision-making in the context of future challenges.[3] The core principles of foresight methods are to explore uncertainty over longer horizons by combining future-oriented, knowledge-based creativity, interaction, expertise, and evidence, enabling stakeholders to anticipate potential scenarios and develop proactive strategies.[4–6]The RRI paradigm incorporates reflexivity, responsiveness, deliberation, and anticipation to make scientific and technological progress more aligned with societal needs and values.[7] By incorporating foresight into AI and ML development in a more global context, the healthcare sector can address upcoming challenges, creating a more sustainable, inclusive, and ethical future for healthcare. By leveraging advanced predictive analytics, these technologies can identify critical issues, like drivers, barriers, opportunities, and threats, much like the Technology Horizon project.[8,9] This proactive approach enables healthcare systems to better prepare for technological innovations and can guide strategic planning, education, and training, fostering a more sustainable, inclusive, and ethical future for healthcare.Foresight methodologies, such as scenario planning, the Delphi method, and trend analysis, help anticipate possible futures of AI development. Scenario planning explores diverse, plausible AI futures under uncertainty; the Delphi method gathers expert consensus on AI trends and risks in healthcare; and trend analysis identifies ongoing AI research and adoption trajectories. Each approach supports strategic decision-making by illuminating the potential impacts and guiding policy or innovation paths. In addition, our approach builds on the conceptual and methodological innovations proposed by Popper et al,[10–12] integrating two foresight-driven sustainability management frameworks, identifying relationships and risks between four “innovation management” dimensions (context, people, process, and impact).In the European Union’s scientific landscape, the RRI policy is a key component. Its objective is to promote collaborative research outcomes while ensuring that the standards are socially acceptable and that the public is aware of the potential ethical effects of technological products.[13] Therefore, open science and technology design principles with applied ethical and societal regulations are emphasized to foster societal acceptance and responsible use of innovative technologies and to ensure economic success.[14] RRI poses specific challenges associated with transparency, inclusiveness, interpretability of AI technologies, and collaborative sectoral developments and recognizes that tasks should be solved according to precise plans,[15] protecting patients’ “best interests” and fundamental rights.[16] With a growing number of complex challenges, such as handling genetic privacy, biometric surveillance, care robots, and human dignity,[17] it is imperative to regularly address ethical considerations under RRI policies and implement enhanced and safer AI and ML tools for medical innovation.[18] This is also demonstrated by international organizations, like the World Health Organization (WHO) and the Geneva Science and Diplomacy Anticipator (GESDA), which are adopting foresight methods to gain a granular view of future developments, aiming to enable a better future for the next generations.[19,20]AI-driven innovation functions as a strong catalyst for advancements, particularly in healthcare.[21,22] However, the complexity of healthcare data and the rapid development of AI and ML algorithms require a proactive approach to address challenges[23] and ensure their responsible and effective application.[24] Integrating AI with comprehensive healthcare and lifestyle data is a key driver of rapid medical innovation.[25] Research in this field is revolutionizing healthcare, from automating image analysis in radiology and pathology to predicting patient outcomes and customizing treatment plans.[26]One of the key challenges in healthcare foresight is the ethical and moral implications of using AI and ML technologies and methodologies. For example, the potential bias in large datasets raises concerns about the objectivity and reliability of AI-driven foresight models[27] and the degree of human judgment that can be applied [28]. Equally important, privacy and data security are also paramount concerns. AI and ML methods necessitate access to large-scale medical and healthcare data to perform precisely and effectively. Thus, safeguarding rigorous data protection procedures aligned with regulatory requirements is vital to prevent illegal access or misuse of the data.[29] However, implementing AI in healthcare foresight presents legal and regulatory challenges. The fast-paced evolution of AI and ML often outpaces existing laws, highlighting the need for clear guidelines to ensure transparency, accountability, and ethical compliance.[30] There is also concern about the socioeconomic impact—although AI can improve outcomes and efficiency, it may widen existing disparities in access to quality care.[31] High implementation costs and limited expertise could further disadvantage resource-constrained healthcare facilities.A systematic investigation of these challenges is essential to ensure the responsible and constructive implementation of these tools in healthcare, explaining and assessing the probable harmful consequences and implications[32] and solutions. A successful example has been provided by applying federated learning to train machine learning models on brain tumors across multiple simulated locations without sharing patient data. This approach directly addressed data privacy and security, as data never left the local servers of different locations, and only model updates (not patient data) were shared and aggregated centrally.[33]Integrating AI and ML into healthcare represents a significant opportunity, but a forward-thinking approach is essential to address the future challenges and opportunities of these technologies. Foresight methods such as scenario planning and speculative design play a crucial role here, providing frameworks for envisioning potential future scenarios—both positive and negative. They apply to healthcare data standards and can be scaled up to include ecosystem dimensions, societal variables, picture-based data (from molecular pathology to whole-body scan), background affiliations of any type, and civil and organizational settings. These can be incorporated into a wide-ranging multi-causal foresight model.[34] Auger[35] and Schoemaker[36] provide examples of how this can be done by using different foresight techniques, such as the deliberate creation of artifacts (e.g., AI algorithms that might become feasible in the technological sense), forcing the considerations on how these artifacts will interact with crucial realities of everyday aspects in future healthcare. It is crucial to recognize that traditional foresight methods often struggle with the dynamic, data-intensive, and multi-stakeholder nature of healthcare, limiting their ability to adapt to rapid changes and interdependencies. Thus, AI-enhanced models add value by processing vast, real-time data, uncovering hidden patterns, and enabling more responsive, nuanced, and predictive insights for complex healthcare systems. This necessity may drive the evolution of existing foresight models into a more integrated, comprehensive, and multidisciplinary approach (Table 1). The novel, synergistic vision we propose does that and is firmly rooted in the principles of RRI, aligned with the European Commission.[37] This approach bridges the gap between the powerful analytical capabilities of AI and ML and the strategic vision provided by foresight methods.By integrating RRI principles, different stakeholders participate, ensuring that the strategies developed meet the population’s needs.[38] Second, RRI emphasizes the development of clear and understandable AI models that provide transparent insights into their decision-making processes, thereby promoting trust and transparency.[39] Third, RRI principles can also guide the development of robust ethical frameworks for using AI and ML in healthcare.[40]Figure 1 illustrates a multiphase path for integrating foresight methods with large-scale healthcare datasets within an RRI framework. Each phase is designed to ensure responsible data collection, comprehensive analysis, and the development of strategic scenarios to guide policy and decision-making processes.Human-driven foresight involves conducting workshops and interviews with diverse stakeholders, including healthcare professionals, patient advocacy groups, and ethicists. This engagement facilitates the identification of pivotal perspectives, driving forces, issues, and events relevant to the future of healthcare. The process enables the comprehensive scoping of stakeholders’ needs and expectations, ensuring that all relevant parties (i.e., healthcare providers, patients, policymakers, and industry representatives) are engaged.AI- and ML-driven foresight assesses publicly available data (primarily anonymous) collected from various sources, including traditional healthcare datasets and innovative data collection methods focusing on the future. All data collection is done with RRI considerations in mind. Furthermore, as Popper described[4], the most appropriate foresight methods will identify emerging trends and potential risks associated with AI and ML advancements.Human-driven foresight will identify future risks, problems, and significant societal challenges through a structured triangulation process, using experts’ judgment, and compare such findings with existing databases and scientific literature.Natural language processing techniques can identify key themes and emerging trends in expert survey data. Bengio et al[41] provide a comprehensive account of these techniques.Human-driven foresight involves creating a visual representation (system map) of the workforce system. This map includes variables and their inter-relationships, which helps understand the system’s dynamic nature. Creating causal maps, incorporating critical uncertainties and predetermined factors, provides a detailed understanding of the variables influencing the healthcare landscape.AI- and ML-driven foresight will involve collaborative workshops with key healthcare stakeholders (clinicians, policymakers, ethicists) to facilitate the development of scenarios. The scenarios will be validated (qualitatively for internal consistency and semi-quantitatively by expert scoring) and will subsequently serve as a foundation for examining potential challenges and opportunities.Human-driven foresight, focusing on high-impact, high-uncertainty variables, enables the identification of key drivers that could shape future healthcare scenarios.The aim is to develop AI models capable of simulating the potential impacts in each future scenario. The models can apply techniques such as reinforcement learning (e.g., as described by Sutton and Barto[42]) to investigate different policies and assess their effectiveness in each scenario.[43]Develop scenarios that describe challenging futures allows stakeholders to explore and prepare for various possible outcomes.AI- and ML-driven foresight research is conducted iteratively, incorporating continuous feedback from healthcare stakeholders.This phase combines human insights with advanced AI-driven analyses to create robust, actionable scenarios that effectively guide policy and decision-making processes. The RRI framework provides the foundation for this phase, underscoring the significance of inclusion, responsiveness, and anticipation. This approach ensures a comprehensive and balanced development process by prioritizing ethical implications, societal impact, and environmental sustainability alongside technical and economic considerations. The result is a set of responsible, inclusive, and forward-thinking strategies that guide the future of healthcare.Integrating AI and ML technologies with healthcare and foresight methods is beneficial and essential for tackling the complexities of modern health challenges. This synergistic approach ensures that deploying these technologies is both responsible and effective, fostering a future where health outcomes are equitable and aligned with human values. This includes the creation of frameworks for continuous feedback between AI and ML systems and foresight tools to ensure the development of adaptive and responsive healthcare strategies. This article presents a systematic approach to doing so has been presented in six distinct, sequential steps that help achieve this integration transparently and efficiently.

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