Drug-device combination products introduced a new dynamic on medical product development, regulatory approval, and corporate interaction that provide valuable lessons for the development of new generations of combination products. This paper examines the case studies of drug-eluting stents and transdermal patches to facilitate a detailed understanding of the challenges and opportunities introduced by combination products when compared to previous generations of traditional medical or drug delivery devices. Our analysis indicates that the largest barrier to introduce a new kind of combination products is the determination of the regulatory center that is to oversee its approval. The first product of a new class of combination products offers a learning opportunity for the regulator and the sponsor. Once that first product is approved, the leading regulatory center is determined, and the uncertainty about the entire class of combination products is drastically Our analysis also suggests that this decision influences the nature (pharmaceutical, biotechnology, or medical devices) of the companies that will lead the introduction of these products into the market, and guide the structure of corporate interaction thereon.

Regenerative medicine products have characteristically shown great therapeutic potential, but limited market success. Learning from the past attempts at capturing value is critical for new and emerging regenerative medicine therapies to define and evolve their business models as new therapies emerge and others mature. We propose a framework that analyzes technological developments along with alternative business models and illustrates how to use both strategically to map value capture by companies in regenerative medicine. We analyze how to balance flexibility of the supply chain and clarity in the regulatory pathway for each business model and propose the possible pathways of evolution between business models. We also drive analogies between cell-based therapies and other healthcare products such as biologicals and medical devices and suggest how to strategically evolve from these areas into the cell therapy space.

To the editor What people call artificial intelligence (AI) has begun to permeate our work and home environments. It provides customer service to consumers, suggests travel routes, and figures out when to turn up thermostats in our homes. It promises to empower precision medicine, handling of medical records, and eventually even replace human drivers. Professionals of all sorts turn to AI applications as "partners" in the work they do. How much should you believe? And most importantly, how can it help neurosurgeons? So-called generalized intelligence remains a distant, elusive aspiration. But there are ample opportunities to avail ourselves to the tools of AI to push the envelope and help discover and answer new questions in myriad fields, including neurosurgery. That requires understanding what the tools can do and how to phrase problems in neurology and neurosurgery to overcome the many limitations of these AI tools and make the most out of them. When the editors of Neurospine suggested we write a commentary, another particularly intriguing opportunity rose to mind: understanding how the neurology and neurosurgery community might benefit from the tools of AI could also help AI itself. The AI community has always hoped to gain inspiration from the way our brains (and entire perceptual and mechanical apparati) might work. Cognitive science has provided some of that, but as computational technology makes AI tools increasingly accessible for neurosurgery research, is there room to imagine collaborations that inform new opportunities in neurosurgery and new insights for AI following from a finer understanding among computer scientists of the phenomenally complex, robust architectures that support what we call "intelligence"? To get there, we need a shared understanding of what AI tools can and cannot yet do. Think of there being 2 ways to use AI. One is typically associated with analytics, regression, classification models, and statistical learning. These tools make the most sense when you have plenty of data. As long as you reduce a problem to a single factor and you have enough data, these tools can power fairly sophisticated software that interprets medical images, anticipates outcomes, or helps spot correlative trends you had not noticed. In these cases, AI tools are not providing fundamentally "new" insights; this is just modeling that may be extraordinarily complex and beyond most human comprehension. And just about everyone seems to think the magic formula for this is more data. Using AI tools this way is the most common and simplest. It is also the source of much of the common confusion-and, frankly, panic-about AI, which most people tend to think of as doing what humans already do, but better-such as outperforming humans at, say,

The agreement of our results with other sources indicates that our premise regarding latent expert knowledge holds. The disease relationships unique to our network may be used to generate hypotheses for future biological and clinical research as well as drug repurposing and design. Our results provide an example of using experimental data on humans to generate biologically useful information and point to a set of new and promising strategies to link clinical outcomes data back to biological research.

At its genesis, no thing about an eventual innovation is new. It is only in hindsight that the stories of innovations become streamlined, linear accounts of success. Actual innovating, like learning, is a highly nonlinear process. To get started, all you need is a hunch about a real-world problem; a set of parts and access to a community of people to render the problem tangible; a strategy to engage in trial and error; and an appetite to learn by being productively wrong first. You learn about the problem as you bring together those people and parts. At the beginning, there is an abundance of paths forward and no “best” path is defined. The path is full of choices, not formulas, and there are many potential outcomes.