The future of health care may change dramatically as entrepreneurs offer solutions that change how we prevent, diagnose, and cure health conditions, using artificial intelligence (AI). This article provides a timely and critical analysis of AI-driven health care startups and identifies emerging business model archetypes that entrepreneurs from around the world are using to bring AI solutions to the marketplace. It identifies areas of value creation for the application of AI in health care and proposes an approach to designing business models for AI health care startups.

Entrepreneurship education is a key beneficiary of design thinking's recent momentum. Both designers and entrepreneurs create opportunities for innovation in products, services, processes, and business models. More specifically, both design thinking and entrepreneurship education encourage individuals to look at the world with fresh eyes, create hypotheses to explain their surroundings and desired futures, and adopt cognitive acts to reduce the psychological uncertainty associated with ambiguous situations. In this article, we illustrate how we train students to apply four well-established cognitive acts from the design cognition research paradigm-framing, analogical reasoning, abductive reasoning, and mental simulation-to opportunity creation. Our pedagogical approach is based on scholarship in design cognition that emphasizes creating preferred situations from existing ones rather than applying a defined set of tools from management scholarship. In doing so, we provide avenues for further development of entrepreneurship education, particularly the integration of design cognition.

This article addresses gaps about abductive reasoning—widely considered key to design‐thinking but rarely detailed in design‐thinking and innovation literatures—by examining two types of abduction; identifying impediments to it; and proposing the promise of Artificial Intelligence (AI) to mitigate those impediments. Contrasting with the deductive and inductive approaches that dominant problem‐solving, we distinguish and elucidate explanatory abduction and innovative abduction in problem finding, where the problem to solve is itself uncertain. We argue these are appropriate for generating innovative problem‐finding ideas. Focusing thenceforth on problem finding alone, the heart of the article proposes a comprehensive conceptual model of innovative idea generation in that more ambiguous, complex, under‐researched but exciting problem space. The model details three chief stages: (1) problem search frame, combining leadership’s vision and innovators’ knowledge; (2) generating abductive hypotheses from often‐surprising observations and their synthesis into insights; and (3) evaluating abductive hypotheses, against the novel quality criteria of plausibility and relevance. Among cognitive impediments we show how the downsides of mental model, limited cognitive load and exemplifying heuristics and cognitive biases, such as confirmation bias, can hinder each stage. Conversely, we examine how support from AI can help human innovators improve the quantity, speed, and quality of their innovative idea generation.

No abstract