The analysis of health technologies has long served as a lens for understanding technological change in science-based industries (Pavitt 1984). Cumulative learning and uncertainty are defining characteristics of medical innovation, which is the outcome of a system of interactive learning rather than a linear sequence from science to market (Rosenberg, 1982; Gelijns and Rosenberg, 1994). Since knowledge production is distributed across universities, firms and clinical environments, the managerial challenge concerns not only firm-level discovery processes, but also the coordination of learning across organisational and disciplinary boundaries. Recursive feedback between research, technology development gives rise to evolutionary trajectories of knowledge development (Mina et. al, 2007), punctuated by radical breakthroughs associated with opportunities for new firm entry and industrial renewal (Orsenigo et al. 2001). The late-twentieth-century biotechnology revolution redefined pharmaceutical R&D management (McKelvey 1996; Munos 2009) since the transition to molecular and genomic platforms required new governance mechanisms for alliances, intellectual property and risk-sharing (Powell et al. 1996; Pisano 2006; Leten et al., 2022).

Recent work situates medical innovation in a new data-intensive and platform-based paradigm for both the biopharma and medical device sectors. Artificial intelligence, bioinformatics and connected manufacturing are transforming discovery, linking digital infrastructure with organisational design (Miozza et al., 2024; Topol 2019). Evidence suggests that while digitalisation accelerates knowledge generation, R&D efficiency continues to decline (Fernald 2024), highlighting the challenge of converting data availability into productivity. Studies of AI and data-driven discovery pipelines show that algorithmic predictions such as GWAS generate both opportunities and noise, requiring new routines for filtering and validation (Tranchero,2024). Meanwhile, work on platform technologies and enabling knowledge infrastructures indicates that shared modular systems (e.g., mRNA or viral vectors) can support economies of scope and inter-organisational learning (Jones et al., 2025). Data-driven R&D management thus centres on orchestrating knowledge flows and platform reuse rather than single-product development.

Across pharmaceuticals, biotech, diagnostics and devices, recent research converges on organisational learning and interdisciplinarity as key levers of progress. The integration of medical and engineering competencies supports complex innovation in hybrid fields such as medical robotics and digital health, but persistent productivity challenges and global inequalities in innovation capacity and diffusion pose several unresolved problems.

For the proposed track we welcome contributions focused on, but not limited to, the following questions:
– How are digital and AI tools changing scientific search and R&D decision-making in medical innovation?
– What forms of governance and strategic behaviours balance open collaboration and proprietary control?
– What are the effects of platformisation and modular R&D on scientific and technological exploration, as well as exploitation?
– How can policies better connect basic research, translational infrastructure and industrial incentives to sustain health innovation locally and globally?
– How do data integration and automation reshape organisational boundaries, roles and skill requirements in R&D-intensive firms?
– What patterns of cross-sector and interdisciplinary collaborations are emerging, and with what consequences on the division of labour and productivity within and beyond project lifecycles