This evolution indicates an expansion from industrial uses into diverse fields, including healthcare [61], [59]. The core functionalities of digital twins include an accurate mirroring of their physical counterparts, capturing all associated processes in a data-driven manner, maintaining a continuous connection that synchronizes with the real-time state of their physical twins, and simulating physical behavior for predictive analysis [85]. In the context of healthcare, a novel extension of this technology manifests in the form of Human Digital Twins (HDTs), designed to provide a comprehensive digital mirror of individual patients. HDTs not only represent physical attributes but also integrate dynamic changes across molecular, physiological, and behavioral dimensions. This advancement is aligned with a shift toward personalized healthcare (PH) paradigms, enabling tailored treatment strategies based on a patient's unique health profile, thereby enhancing preventive, diagnostic, and therapeutic processes in clinical settings [44], [50]. The personalization aspect of HDTs underscores their potential to revolutionize healthcare by facilitating precise and individualized treatment plans that optimize patient outcomes [72]. Although the potential of digital twins in healthcare has garnered much attention, practical applications remain newly developing, with critical literature highlighting that many implementations are still in exploratory stages [59]. Notably, institutions like the IEEE Computer Society and Gartner recognize this technology as a pivotal component in the ongoing evolution of healthcare systems that emphasize both precision and personalization [31], [89].

The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This continuum ensures the coherent integration of computational resources and services from centralized data centers to edge devices, facilitating efficient and adaptive computation and application delivery. AI-SPRINT has achieved significant scientific advances, including streamlined processes, improved efficiency, and the ability to operate in real time, as evidenced by three practical use cases. This paper provides an in-depth examination of these applications -- Personalized Healthcare, Maintenance and Inspection, and Farming 4.0 -- highlighting their practical implementation and the objectives achieved with the integration of AI-SPRINT technologies. We analyze how the proposed toolchain effectively addresses a range of challenges and refines processes, discussing its relevance and impact in multiple domains. After a comprehensive overview of the main AI-SPRINT tools used in these scenarios, the paper summarizes of the findings and key lessons learned.

As scientists, we are no stranger to skepticism, having been taught to look at everything critically. While doling out skepticism, we also come across it often when working with other healthcare leaders or fielding questions from audiences at conferences or pitching an AI approach to investors. Much of the healthcare industry is still stuck in the 20th century, and hence, it is not so surprising that new technologies such as an AI-driven approach to biopharma may be met with raised eyebrows and thought to be doomed to failure from the outset. On the one hand, we have people saying that AI could revolutionize biopharma and help us to discover new treatment options, with Deloitte predicting that the AI/biopharma industry will be worth $3.88 billion by 2025. On the other, we have Elon Musk warning that AI could spell the end of civilization as we know it.

At the Current Trends in Biotherapeutics Workshop, Alexandre Le Bouthillier, PhD, Co-Founder, Imagia, presented as part of Session 3: Future Trends in Translational Medicine. His talk was called "Personalized Healthcare with Artificial Intelligence." The Clinical Translation Education Group (CTEG) hosted its 2019 workshop on current trends and innovations in cell and gene therapy with an emphasis on disruptive technologies. The session introduced new tools and strategies that are shaping where the biotherapeutic field is headed. The workshop took place in Toronto on September 29, 2019.

Up until very recently, computers needed a complicated and extremely precise set of instructions in order to accomplish even the simplest of tasks. Who among us remembers programming via punch cards? Computer programming languages have evolved over the years, but the biggest step has been moving towards the elimination of complicated programming. In other words, teaching computers to learn for themselves, dubbed machine learning. Because machine learning is such a promising leap forward in technological ability, it has the very real potential to affect every person in every field of business in the near future.

The evolution in health care to greater support for self-managed care is escalating the demand for e-health systems in which patients can access their personal health information in order to ultimately partner with providers in the management of their health and wellness care. At present, unfortunately, patients are seldom able to easily access their own health information so, as a result, it is often difficult for patients to enter into a dialogue with their healthcare providers about treatment and other options. One truism seems to be constantly ignored: it is not possible for patients to actively manage their health without the requisite information. Health information should be made available through "any time, anywhere" delivery: outside the physician's office or hospital, in the home or other personal setting, on a variety of multimedia information devices. We believe that personalization of health information will be a key element in effective self-managed healthcare.