Browsing by Author "Crispin, Mireia"

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    Artificial intelligence in healthcare: a technological perspective
    (IE University, 2020-12-18) Gallego, Marcos; Berman, Adam; Crispin, Mireia; https://ror.org/02jjdwm75
    Artificial intelligence (AI) is one of the key technological innovations shaping our modern world. The growing availability of digital health data, combined with the newest technical advancements in computing and data science, form the perfect environment for data-driven, AI-powered medicine to flourish. Many of the challenges presented by AI arise from its very foundation, from biases to transparency or generalizability. This chapter therefore focuses on presenting the basic ideas behind AI algorithms. In particular, it focuses on the different types of learning (supervised vs. unsupervised, shallow vs. deep), and discusses some of the most popular ways in which AI methods have been applied in healthcare. We then discuss the main technological pitfalls, and summarize them into four key questions on transparency, overfitting, performance and robustness that AI models with translational ambitions should be able to address adequately. Finally, AI has the potential to go beyond simple applications, and become one of the key tools to connect medical research and clinical practice. We suggest that, if the field progresses steadily despite all the complexities, it could become the backbone of a knowledge-generating feedback loop between the two.
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    Digital health regulation landscape and data challenges
    (IE University, 2021-09-01) Crispin, Mireia; Gallego, Marcos; https://ror.org/02jjdwm75
    AI and digital technologies hold promise for a more efficient and effective care delivery. In order to maximise the potential of these emerging technologies and enable their effective integration into European healthcare systems, a concerted, multi-stakeholder effort in data is necessary, with a particular focus on:  Top-down strategic approach for structures, standards & infrastructure by identifying key areas of systemic need where a certain structure and set of standards are key for primary and secondary uses. Bottom-up approach for solution finding and development, by enabling local actors to identify key local characteristics that will shape the kinds of solutions that are needed, with subsequent co-creation. Integration of the topdown and the bottom-up approaches by defining a set of standards for data capture and data exchange, and socialising best practices.
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    Innovation, Sustainability and the Future of Healthcare : How is artificial intelligence reshaping healthcare in Europe? - Executive Report
    (IE Center for the Governance of Change, 2020-07-15) Crispin, Mireia; Gallego, Marcos; Ellena, Javier; Gerhold, Malte; Lezaun, Javier; Unda, Arantxa; Ahmad, Saif; Berman, Adam; Cirkovic, Stevan; Goldsworthy, Christopher; Halai, Dina; Jeffrey, Genevieve; Law, Samantha; Machado, Diogo; O'Carrigan, Brent; Pesapane, Filippo; Sissons, Amanda; https://ror.org/02jjdwm75
    The studies included in this report show that the integration of Artificial Intelligence (AI) technologies in the European healthcare setting presents a series of unique challenges that will require large, collaborative and transparent efforts crossing boundaries of profession and geography. 
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    Technical advancements and pathway integration
    (IE University, 2021-09-01) Crispin, Mireia; Gallego, Marcos; https://ror.org/02jjdwm75
    AI and digital technologies hold promise for a more efficient and effective care delivery. In order to maximise the potential of these emerging technologies and enable their effective integration into European healthcare systems, a concerted, multi-stakeholder effort is necessary, with a particular focus on: Pursuing need-oriented innovation by identifying key areas of systemic need where high-impact innovative solutions can lead to meaningful change for patients and healthcare professionals. Increasing funding of innovative medical technologies by reducing investment risk for private venture capital funds to support highly innovative early-stage ventures. Enabling early real-world testing of post proof-of concept digital products to allow for early user feedback and thus more effective product development. Democratising access to healthcare data by nation-wide and pan-European initiatives aimed at data harmonisation and effective sharing of clinical data. Creating inclusive health technologies by educating future healthcare professionals on tech innovation and entrepreneurship, and by prioritizing and championing the role of patients in technology creation, testing, and implementation.