This study aims to explore the current landscape of Telemedicine, Arti­ficial Intelligence, and Digital Therapeutics in Italy, with a focus on iden­tifying key development trends and existing challenges.

DESIGN AND METHODS We conducted a comprehensive review of re­cent scientific literature, regulatory frameworks, and policy documents relevant to these three domains.

RESULTS While technological progress in these areas has been nota­bly rapid in recent years, the pace of integration into clinical workflows, healthcare organizational structures, and professional competencies remains uneven and relatively slow.

CONCLUSIONS Today, the primary challenge lies not in introducing new technologies, but in establishing the institutional and organiza­tional conditions necessary to ensure that innovation is sustainable and capable of delivering real value within the healthcare system.

KEY WORDS Digital Health; telemedicine; artificial intelligence; digital therapeutics; innovation.

The Digital Health promises to radically transform the approach to di­abetes management, outlining an integrated ecosystem that includes smart devices, electronic medical records, remote monitoring plat­forms and mobile applications. This article analyzes the main digital tools involved in clinical practice, with particular attention to efficacy, potential advantages and critical issues to be addressed. The evolution of the electronic medical record, integrations with other platforms and apps for diabetes monitoring and management can improve treatment adherence, face therapeutic inertia and provide solutions to growing healthcare needs with greater equity in the allocation of limited health­care resources, also through proactive interventions by diabetes teams. Mobile apps on smartphones can also encourage patient self-manage­ment by tracking improvements and clinical outcomes. However, signif­icant obstacles such as system fragmentation, lack of interoperability, inequalities in access to digital solutions persist. In conclusion, digital therapies in diabetology represent a concrete opportunity to improve the effectiveness of care, but require systematic integration into care pathways, adequate regulatory support and investment in operator training and patient empowerment.

KEY WORDS Digital Health; diabetes; electronic medical record; smart pen; AID; mobile health; interoperability; self-management.

Digital Therapeutics (DTx) represent an innovation in the treatment of chronic diseases and mental disorders. They are software-based therapeutic solutions that, unlike generic health apps, can be prescribed by physicians after regulatory approval, following rigorous clinical trials (RCTs). They integrate Artificial Intelligence (AI) and digital tools to provide personalized care and improve treatment adherence.

DTx can be classified as Stand alone (independent), Associated (integrated with other treatments), or Combined (used alongside specific drugs). They must comply with regulatory standards, have a measurable therapeutic effect, and enable real-time monitoring. DTx follow a structured process from market entry to prescription, with major clinical applications in diabetes, mental health, neurological disorders, and substance abuse treatment.

DTx leverage advanced technologies such as AI, gamification, virtual reality, and adaptive algorithms to personalize treatments and interact with patients. However, their large-scale adoption is still limited by technical and regulatory challenges.

The approval process follows drug-like pathways (RCTs) to assess safety, efficacy, and potential side effects, yet there is currently no unified international regulation. Due to their direct therapeutic intervention with patients, integration with specific evaluation models (HTA) will be essential for official recognition.

DTx represent the future of digital medicine, improving chronic disease management and access to care. However, their development requires regulatory adjustments, extensive clinical studies, and broader acceptance by both physicians and patients.

KEY WORDS digital therapeutics (DTx); digital medicine; artificial intelligence; therapeutic innovation; future of healthcare.

A Digital Twin (DT) is a virtual replica of an object, designed to repre­sent its behavior in different conditions. It uses simulation to repro­duce the functioning of a system (or an organism, or a part thereof) in a virtual environment. Using artificial intelligence (AI) and machine learning (ML), it analyzes operational data and generates predictions to support decision-making. A DT allows you to monitor and dynamically intervene on the critical factors of a phenomenon, preventing malfunc­tions or worsening before they occur. It is continuously updated with real-time data, ensuring an accurate dynamic representation of the system. The ability of the DT to prevent and reduce risks has made it a tool of great interest in the healthcare and diabetes fields, thanks to its potential in improving glycemic control and preventing complications, in what can be defined as a Health Digital Twin (HDT). This paper ana­lyzes the functioning of DT and enabling technologies, with examples of application of the HDTs in diabetes and other therapeutic areas. Fi­nally, the article highlights the challenges, including ethical concerns and the opportunities offered by HDTs, whose development must be guided by multidisciplinary teams able to grasp all the implications and maximize its potential.

KEY WORDS digital twin; precision medicine; artificial intelligence; data integration.

The doctor-patient relationship, a cornerstone of the care process, demands effective communication, empathy, attentiveness to the patient’ssubjective exp erience, and the ability to foster mutual trust. Nevertheless, organizational challenges, time constraints during consultations, fragmented care pathways, bureaucratic pressures, and personality dynamics on both sides can all jeopardize this delicate bond. Digital technologies offer immense transformative potential, capable of both strengthening and straining this relationship. Telemedicine, electronic health records, health apps, artificial intelligence, and virtual assistants can enhance access to care, reduce wait times, personalize treatment plans, promote patient autonomy and engagement, support ongoing and multichannel communication, and enable the rapid exchange of information. When thoughtfully leveraged, these tools can contribute significantly to enriching the doctor-patient connection. Conversely, diminished human contact, the depersonalization of communication, unrealistic patient expectations, and an overreliance on automated systems risk eroding empathy and reducing the therapeutic relationship to a sterile exchange of data. Moreover, disparities in access to technology, as well as concerns surrounding data security and privacy, represent critical challenges that must not be underestimated. It is therefore imperative that new technologies be employed not merely as functional tools, but as means to strengthen trust, empower patients, and preserve the emotional dimension and personalization of care. While continuous monitoring and data sharing can enhance patient awareness and autonomy, they also require robust educational and psychological support to mitigate potential anxiety or isolation. If wisely guided, technological innovation holds the promise of reshaping the doctor-patient dynamic into a more balanced and participatory partnership, where patients, empowered by intelligent tools, become active protagonists of their own healthcare journey. It is within this delicate equilibrium that the future of the doctor-patient relationship in the digital era will ultimately be determined.

KEY WORDS doctor-patient relationship; digital health interventions; augmented reality; non-immersive virtual reality simulation; artificial intelligence and education.

Clinical Decision Support Systems (CDSS) are fundamental tools for as­sisting physicians in the decision-making process, thanks to their abili­ty to analyze clinical data and provide diagnostic or therapeutic recom­mendations. The literature classifies them mainly as knowledge-based systems, which employ IF-THEN rules grounded in expert clinical expe­rience, and machine learning systems, which use statistical models to identify data patterns.

Despite their potential, CDSS face limitations hindering their effective­ness and adoption. Many focus solely on single pathologies, overlo­oking the complexity of comorbidities and the patient’s multidimen­sional nature. Moreover, a lack of interoperability often necessitates manual data entry, risking errors and incomplete information, which negatively impacts performance. Physician diffidence, stemming from technical issues and perceived limited control, further impedes their uptake.

Addressing Digital Health (DH) needs requires evolving CDSS toward greater interoperability, telemedicine integration, multidisciplinary management, and personalized care.

Of particular interest is the ongoing challenge of automatically and dynamically calculating individual patient risk for complications or worsening of clinical conditions. This relies on processing real-time data from vital signs, health records, and questionnaires. Integrating this information into a Medical Expert System (MES) could significantly enhance clinical decision support. This article focuses MES characteri­stics and their role in DH, showing a telemedicine application for mana­ging complex chronic heart failure patients.

KEY WORDS clinical decision support systems (CDSS); medical expert sy­stem (MES); digital health (DH); artificial intelligence (AI); digital twins (DT).

In this historical era of the evolution of health systems, digital devel­opment, monitoring systems and the possibility of sharing health in­formation the National Health Record 2.0 offers new opportunities and prospects for the development of models of care in chronic diseases. The diabetologist expert in Digital Health (Digital Diabetologist) must therefore acquire the ability to use tools and methodologies for the construction, implementation and governance of e-health pathways for the management of Chronicity. The priority characteristics of the new professional profile must reflect organizational skills (construction of digital PDTA), collaboration (digital networking) and, communica­tion (digital communication) through digital tools.

The areas of knowledge and expertise that allow the construction of this expert figure are recognized in:

1. Ability to organize and implement structured digital pathways with Telemedicine (TLM) in its various forms (televisit, telemonitoring, tele­consultation, telereporting).

2. Knowledge of Artificial Intelligence (AI) tools and applications in healthcare.

3. Knowledge of digital therapeutics.

4.Skills in digital team work, networking and digital problem solving.

5. Skills in digital communication.

Since the development of Digital Health is in the start-up and consol­idation phase both for professional development and organizational and administrative aspects, AMD has established a board of study in the field of Digital Health which, among its various activities, has iden­tified and created a certification profile for the expert diabetologist and started a path aimed at improving knowledge and skills in this field.

KEY WORDS Digital Health, professional profile, digital skills, digital training

In recent years, medicine has undergone a profound transformation driven by the convergence of clinical science and digital innovation. At the heart of this shift are digital biomarkers – tools that allow for re­al-time observation, interpretation, and prediction of disease dynamics with unprecedented accuracy. These are not just data points, but con­tinuous signals passively collected from individuals’ daily lives, capable of capturing subtle physiological and behavioral changes often invis­ible to conventional medicine. This article explores the role of digital biomarkers in managing diabetes mellitus, with a focus on specific ap­plications such as nocturnal hypoglycemia, diabetic foot, and retinop­athy. It examines both technological advances – including wearables, predictive algorithms, and machine learning – and the ongoing chal­lenges related to scientific validation, regulation, and ethics. The result is a nuanced and realistic perspective that presents digital biomarkers not as magic bullets, but as powerful tools to be carefully integrated into a new precision medicine paradigm.

KEY WORDS digital biomarkers; digital phenotyping; extended pheno­type; digital footprints; disease phenotype.

The future of healthcare is increasingly uncertain and constantly evolving. Diabetology, like many other medical disciplines, is influenced by techno­logical advancements, economic changes, and new social needs. In this context, foresight becomes an essential element for anticipating transfor­mations and building resilient strategies. It is not about predicting with cer­tainty what will happen, but rather exploring possible futures and prepar­ing to manage the opportunities and challenges that will emerge.

The analysis of volatility, ambiguity, and turbulence in the healthcare sector highlights the need for a systemic and proactive approach. Dis­tinguishing between risk and uncertainty allows health professionals to adapt to complex scenarios and develop more effective intervention models. Systems thinking helps to understand the interconnection be­tween digital innovations, healthcare policies, and patient behaviors, fostering an integrated and sustainable vision of the future of healthcare.

Through tools such as trend analysis, scenario planning, and backcast­ing, foresight offers practical methods to guide change and promote innovative solutions. This article explores the application of foresight to diabetology and digital health, outlining strategies to transform un­certainty into opportunity. The future of healthcare is not something to endure, but to build: with the right methodologies, we can guide it toward more equitable, sustainable, and innovative scenarios.

KEY WORDS foresight; healthcare; diabetology; innovation; scenario planning.

Digital skills are a key aspect of innovation in our National Health Service for all the stakeholders: patients, clinicians, technicians, IT professionals and managers. Digital skills refer to the ability and practical experience required to use information technologies for work and communication.

At the institutional level in Italy, there is a strong strategic focus on enhan­cing the digital skills of healthcare professionals and their continuous trai­ning in the field of “Digital Health”. Back in 2016, the national plan “Piano Nazionale per la Cronicità” recognized telemedicine as a general and specific objective for various conditions, including diabetes. In the 2024 update, an entire chapter is dedicated to digital healthcare. Likewise, the “Piano Nazionale di Ripresa e Resilienza” (PNRR) has allocated significant resources for the digital transformation of healthcare and staff training.

As in any other healthcare and treatment-related process, training to­ols are at the heart of innovation. Continuing Medical Education (CME) and university education provide recognized certifications or credits for active personnel. CME is primarily aimed at updating previously acquired skills and is mainly based on conferences, where the earned credits contribute to the mandatory training requirements for each pro­fessional. University education, on the other hand, helps professionals acquire new skills, sometimes qualifying them for professional activi­ties, through degree programs, specialization courses, and first- and se­cond-level master’s degrees. However, new training strategies are also emerging, offering valuable alternatives.

KEYWORDS training, lifelong learning, professional development, di­gital courses

The digital transformation of healthcare and the deployment of genera­tive artificial intelligence technologies raise unprecedented ethical and governance challenges. This article critically reviews risks related to reliability, accuracy, algorithmic biases, decision-making automation, and privacy protection, specifically focusing on AI tools and other dig­ital health solutions (telemedicine, health apps, wearable devices). It highlights the importance of participatory and transparent governance, actively involving healthcare professionals, patients, and civil society, clearly defining the roles and responsibilities of each stakeholder. The paper underscores the need for sustainable and equitable policies that promote social inclusion and resource efficiency, providing practical recommendations for ethical governance in Digital Health.

KEY WORDS Digital Health; artificial intelligence; ethics; accountability; participatory governance.

The concept of Digital Health encompasses the set of information and communication technologies (ICT) that benefit human health and organizational systems. It emerges from the convergence of digital technologies with the fields of health, healthcare, lifestyle, and socie­ty, with the aim of improving the efficiency of healthcare delivery and making it more personalized and precise. Digital Health is a growing reality that is introducing new solutions, requiring continuous and rapid updates both in skills and in the language, which is becoming increasingly enriched and specialized across different domains. Unfor­tunately, the speed of evolution has often led to confusion in the use of various terms, making it clear how strategic it is to share a common lexicon for identifying tools and methodologies for digital applications in medicine. This common understanding supports diabetologists and the healthcare team, patients, and other professionals working in the healthcare sector (e.g., physicists, engineers, etc.) in optimizing care pathways and outcomes.

KEY WORDS Digital Health, digital skills, artificial intelligence.