Background The integration of Artificial Intelligence (AI) into healthcare services and technologies offers substantial potential for personalised medicine. The Autonomous Province of Trento (Italy) provides a unique setting for AI-driven healthcare research, due to its unified healthcare system, advanced IT infrastructure, and strong public-private collaborations. This paper explores an initiative aimed at improving healthcare accessibility and promoting innovation through AI in three clinical domains: Cardiology, Diabetic Retinopathy, and Paediatric Ophthalmology. Methods The project employs a structured approach, involving specialised working groups addressing clinical needs, AI techniques, legal and ethical compliance and data management. The initiative aims to develop predictive models aligned with European and national data protection regulations. Results Three primary clinical objectives were defined: estimating individual risk profiles in heart failure patients, personalising screening intervals for diabetic retinopathy, and supporting early diagnosis of anterior segment opacities in infants. Data relevant for the selected outcomes were identified. A dedicated platform for compliant, secure and structured access to data was developed. A data analysis plan was designed, including data processing, models selection, optimization and evaluation. All research protocols were approved by the local Ethics Committee. Discussion The initiative investigates the AI potential to improve clinical outcomes and establish a sustainable, personalised healthcare system. Key challenges include data accessibility, regulatory compliance, and adherence to ethical standards. The project's comprehensive framework offers a model for broader applications. Future research will focus on model validation and expanding the initiative to other clinical domains. Public Interest Summary This article presents the "Digital Health and Artificial Intelligence" project, an initiative funded by The Autonomous Province of Trento (Italy) to enhance healthcare accessibility and foster innovative healthcare models using technology and Artificial Intelligence (AI). The current work presents the design and preparatory work for the implementation of three AI-based solutions for research purposes, encompassing three areas: i) Cardiology, ii) Diabetic Retinopathy, and iii) Paediatric Ophthalmology. The paper outlines the legal and organizational frameworks, mathematical modelling and data management emphasising the necessity of cross-disciplinary endeavour and collaboration. Overall, this project represents a forward-looking initiative promoting research conducted on citizen data to address healthcare needs through innovative AI-driven approaches in line with legal and ethical standards.

Digital health and artificial intelligence: a research approach to enable sustainable and personalised local healthcare / Moroni, Monica; Novello, Lisa; Malfatti, Giulia; Gios, Lorenzo; Bonmassari, Roberto; Greco, Maurizio Del; Maines, Massimiliano; Moretti, Michele; Inchiostro, Sandro; Romanelli, Federica; Racano, Elisabetta; Maggi, Tania Elena; Fiabane, Valentina; Compagnone, Adele; Filippi, Lorena; Pasquini, Roberta; Betta, Marta; Pavanello, Lucia; Manica, Andrea; Cagol, Diego; Santoprete, Enrico; Cecchetto, Simone; Bincoletto, Giorgia; Conforti, Diego; Nicolini, Andrea; Jurman, Giuseppe. - In: HEALTH POLICY AND TECHNOLOGY. - ISSN 2211-8837. - 2026, 15:2(2026), p. 101149. [10.1016/j.hlpt.2025.101149]

Digital health and artificial intelligence: a research approach to enable sustainable and personalised local healthcare

Bincoletto, Giorgia;
2026-01-01

Abstract

Background The integration of Artificial Intelligence (AI) into healthcare services and technologies offers substantial potential for personalised medicine. The Autonomous Province of Trento (Italy) provides a unique setting for AI-driven healthcare research, due to its unified healthcare system, advanced IT infrastructure, and strong public-private collaborations. This paper explores an initiative aimed at improving healthcare accessibility and promoting innovation through AI in three clinical domains: Cardiology, Diabetic Retinopathy, and Paediatric Ophthalmology. Methods The project employs a structured approach, involving specialised working groups addressing clinical needs, AI techniques, legal and ethical compliance and data management. The initiative aims to develop predictive models aligned with European and national data protection regulations. Results Three primary clinical objectives were defined: estimating individual risk profiles in heart failure patients, personalising screening intervals for diabetic retinopathy, and supporting early diagnosis of anterior segment opacities in infants. Data relevant for the selected outcomes were identified. A dedicated platform for compliant, secure and structured access to data was developed. A data analysis plan was designed, including data processing, models selection, optimization and evaluation. All research protocols were approved by the local Ethics Committee. Discussion The initiative investigates the AI potential to improve clinical outcomes and establish a sustainable, personalised healthcare system. Key challenges include data accessibility, regulatory compliance, and adherence to ethical standards. The project's comprehensive framework offers a model for broader applications. Future research will focus on model validation and expanding the initiative to other clinical domains. Public Interest Summary This article presents the "Digital Health and Artificial Intelligence" project, an initiative funded by The Autonomous Province of Trento (Italy) to enhance healthcare accessibility and foster innovative healthcare models using technology and Artificial Intelligence (AI). The current work presents the design and preparatory work for the implementation of three AI-based solutions for research purposes, encompassing three areas: i) Cardiology, ii) Diabetic Retinopathy, and iii) Paediatric Ophthalmology. The paper outlines the legal and organizational frameworks, mathematical modelling and data management emphasising the necessity of cross-disciplinary endeavour and collaboration. Overall, this project represents a forward-looking initiative promoting research conducted on citizen data to address healthcare needs through innovative AI-driven approaches in line with legal and ethical standards.
2026
2
Moroni, Monica; Novello, Lisa; Malfatti, Giulia; Gios, Lorenzo; Bonmassari, Roberto; Greco, Maurizio Del; Maines, Massimiliano; Moretti, Michele; Inch...espandi
Digital health and artificial intelligence: a research approach to enable sustainable and personalised local healthcare / Moroni, Monica; Novello, Lisa; Malfatti, Giulia; Gios, Lorenzo; Bonmassari, Roberto; Greco, Maurizio Del; Maines, Massimiliano; Moretti, Michele; Inchiostro, Sandro; Romanelli, Federica; Racano, Elisabetta; Maggi, Tania Elena; Fiabane, Valentina; Compagnone, Adele; Filippi, Lorena; Pasquini, Roberta; Betta, Marta; Pavanello, Lucia; Manica, Andrea; Cagol, Diego; Santoprete, Enrico; Cecchetto, Simone; Bincoletto, Giorgia; Conforti, Diego; Nicolini, Andrea; Jurman, Giuseppe. - In: HEALTH POLICY AND TECHNOLOGY. - ISSN 2211-8837. - 2026, 15:2(2026), p. 101149. [10.1016/j.hlpt.2025.101149]
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