Published November 19, 2025 | Version 1.0
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Third Digital Health Monitoring Report. Digital Health & AI Tools for Patients and Citizens

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The Third Digital Health Monitoring Report provides an updated and comprehensive overview of Digital Health Technologies (DHTs) and their evolution within global clinical research, regulatory frameworks, market access, and policy development. Building on the work of the Digital Health Policy Lab (Indicon Società Benefit and University of Milan). The report examines a corpus of around 1,800 clinical studies on Digital Health Technologies, from which 254 Digital Therapeutics (DTx) trials were identified and analyzed in depth. These DTx studies show a 54% increase over the past 15 months, with psychiatric disorders, insomnia, depression, and anxiety emerging as the leading therapeutic areas.

At the European level, the report maps the regulatory and reimbursement pathways in Germany, the UK, France, Belgium, and Spain, highlighting Germany’s DiGA model as the most advanced and the UK NICE Early Value Assessment as an emerging mechanism for early adoption of promising technologies. For Italy, the report presents a national mapping of certified patient-managed Digital Medical Devices (pDMDs), an updated overview of DTx availability, and a budget impact analysis estimating €62 million per year for potential reimbursement.

The second part examines innovative clinical applications, including DTx for neurodevelopmental disorders, mental health, and the combined use of digital and pharmacological therapies. The third part focuses on Artificial Intelligence, exploring data analytics, the integration of AI within medical devices, oncology applications, and economic evaluations of DTx across therapeutic areas.

The report aims to support policymakers, healthcare professionals, researchers, and industry stakeholders by offering evidence-based insights to guide the integration of digital health solutions into the Italian National Health Service

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References

  • Wang C, Lee C, Shin H. Digital therapeutics from bench to bedside. NPJ Digit Med. 2023 Mar 10;6(1):38. doi: 10.1038/s41746-023-00777-z. PMID: 36899073; PMCID: PMC10006069.
  • Teepe GW, Glase EM, Reips UD. Increasing digitalization is associated with anxiety and depression: A Google Ngram analysis. PLoS One. 2023 Apr 7;18(4):e0284091. doi: 10.1371/journal.pone.0284091. PMID: 37027368; PMCID: PMC10081798.
  • Bantjes J. Digital solutions to promote adolescent mental health: Opportunities and challenges for research and practice. PLoS Med. 2022 May 31;19(5):e1004008. doi: 10.1371/journal.pmed.1004008. PMID: 35639740; PMCID: PMC9154099.
  • Sander LB, Beisemann M, Karyotaki E, van Ballegooijen W, Cuijpers P, Teismann T, Doebler P, Domhardt M, Baumeister H, Büscher R. Effects of digital cognitive behavioral therapy for depression on suicidal thoughts and behavior: Protocol for a systematic review and meta-analysis of individual participant data. PLoS One. 2023 Jun 8;18(6):e0285622. doi: 10.1371/journal.pone.0285622. PMID: 37289758; PMCID: PMC10249902.
  • Koller C, Blanchard M, Hügle T. Assessment of digital therapeutics in decentralized clinical trials: A scoping review. PLOS Digit Health. 2025 Jun 23;4(6):e0000905. doi: 10.1371/journal.pdig.0000905. PMID: 40549731; PMCID: PMC12185025.
  • Aapro M, Bossi P, Dasari A, Fallowfield L, Gascón P, Geller M, Jordan K, Kim J, Martin K, Porzig S. Digital health for optimal supportive care in oncology: benefits, limits, and future perspectives. Support Care Cancer. 2020 Oct;28(10):4589-4612. doi: 10.1007/s00520-020-05539-1. Epub 2020 Jun 12. PMID: 32533435; PMCID: PMC7447627.
  • van Kessel R, Roman-Urrestarazu A, Anderson M, Kyriopoulos I, Field S, Monti G, Reed SD, Pavlova M, Wharton G, Mossialos E. Mapping Factors That Affect the Uptake of Digital Therapeutics Within Health Systems: Scoping Review. J Med Internet Res. 2023 Jul 25;25:e48000. doi: 10.2196/48000. PMID: 37490322; PMCID: PMC10410406.
  • Bundesinstitut für Arzneimittel und Medizinprodukte website. Access to the Verzeichnis | DiGA-Verzeichnis. Available at: https://diga.bfarm.de/de/verzeichnis
  • National Institute for Health and Care Excellence. NICE website | Early Value Assessment (EVA) for medtech. Available at: https://www.nice.org.uk/what-nice-does/our-guidance/about-medical-technologies-guidance/early-value-assessment-eva-for-medtech
  • The Agence du Numérique en Santé website | Liste des solutions certifiées conformes au référentiel d'interopérabilité et de sécurité des DMN | Document associé. Available at: https://esante.gouv.fr/liste-solutions-certifiees-conformes-referentiel-interoperabilite-securite-dmn
  • mHealthBelgium website | All applications. Available at: https://mhealthbelgium.be/apps
  • Secondo DTx Monitoring Report. Indicon. Available at: https://www.indicon-innovation.tech/event/dtx-monitoring-report-2024/
  • Bundesinstitut für Arzneimittel und Medizinprodukte website. Access to the Verzeichnis | DiGA-Verzeichnis. Available at: https://diga.bfarm.de/de/verzeichnis
  • Ministero della Salute website. Elenco dei dispositivi medici. Available at: https://www.salute.gov.it/interrogazioneDispositivi/RicercaDispositiviServlet?action=ACTION_RICERCA
  • Ministero della Salute website. Classificazione Nazionale Dispositivi Medici (CND) - Come modificata dal DM 10.11.2021 - Categorie dei Dispositivi Medici. Available at: https://www.salute.gov.it/new/sites/default/files/imported/C_17_pagineAree_328_22_file.pdf
  • Medicina digitale per la prevenzione e la cura. Ospedali Galliera Genova 17 giugno 2025. Available at: https://www.galliera.it/ospedale-informa/notizie/medicina-digitale-per-la-prevenzione-e-la-cura
  • Secondo DTx Monitoring Report. Indicon. Available at: https://www.indicon-innovation.tech/event/dtx-monitoring-report-2024/
  • One Health Vision. Alleniamo la memoria. Available at: https://onehealthvision.com/app/alleniamo-la-memoria/
  • One Health Vision. BuildAttention. Available at: https://onehealthvision.com/en/app/buildattention-attenzionefunzioni-esecutive/
  • One Health Vision. Leggo Facile. Available at: https://onehealthvision.com/en/digital-therapeutics/leggo-facile-per-trattare-dislessia/
  • One Health Vision. Balbus 5.0. Available at: https://onehealthvision.com/libri/balbus-5-0-tecnologia-neuroscienze-e-intervento-integrato-per-la-balbuzie/
  • Cerebrum VR website. Available at: https://cerebrumvr.com/
  • One Health Vision. Communicator AI. Available at: https://onehealthvision.com/en/communicator-ai-la-rivoluzione-nella-riabilitazione-dellafasia/
  • One Health Vision. LegoQuiz. Available at: https://onehealthvision.com/en/app/logoquiz-trattamento-afasia/
  • RESTORATIVE Neurotechnologies. MindLenses Professional. Available at: https://www.restorativeneurotechnologies.com/mindlenses-professional/
  • REHABILITY. Available at: https://rehability.me/index.html#gallery
  • Morecognition. Remo. Available at: https://www.morecognition.com/en/
  • One Health Vision. SelectEat. Available at: https://onehealthvision.com/en/selettivita-alimentare-e-arfid-analogie-differenze-e-trattamento/
  • Tandem Diabetes. Available at: https://www.tandemdiabetes.com/it-it/home
  • One Health Vision. TuttAscolto. Available at: https://onehealthvision.com/en/app/tuttascolto-riabilitazione-udito-ipoacusia/
  • One Health Vision. Volo Bla Bla. Available at: https://onehealthvision.com/en/app/volo-bla-bla-trattamento-disturbo-fonologico/
  • One Health Vision. BuildAttention ottiene la certificazione CE. Available at: https://onehealthvision.com/buildattention-ottiene-certificazione-dispositivo-medico-certificato-ce/
  • Liberness. Innlifes. Available at: https://www.innlifes.com/digital-health/liberness-terapia-italiana-obesita/
  • Polifarma. QPress®. Available at: https://www.polifarma.it/it/blog-e-news/detail/polifarma-laife-reply-e-davi-digital-medicine-insieme-per-una-21414
  • Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM). Verzeichnis der digitalen Gesundheitsanwendungen (DiGA-Verzeichnis).
  • Elenco dei dispositivi medici. Ministero della Salute.
  • Davison, N.J., et al., Cost-Effectiveness Analysis of a Prescription Digital Therapeutic in Type 2 Diabetes. Adv Ther, 2024. 41(2): p. 806–825.
  • Nomura, A., et al., Cost-effectiveness of digital therapeutics for essential hypertension. Hypertens Res, 2022. 45(10): p. 1538–1548.
  • Lewkowicz, D., E. Bottinger, and M. Siegel, Economic Evaluation of Digital Therapeutic Care Apps for Unsupervised Treatment of Low Back Pain: Monte Carlo Simulation. JMIR Mhealth Uhealth, 2023. 11: p. e44585.
  • Atto Camera: 1208. Iter. Available at: https://www.camera.it/leg19/126?leg=19&idDocumento=1208
  • Testo Proposta di legge N. 1208. Available at: https://www.camera.it/leg19/995?sezione=documenti&tipoDoc=lavori_testo_pdl&idLegislatura=19&codice=leg.19.pdl.camera.1208.19PDL0040170&back_to=
  • Testo Proposta di legge N. 2095. Available at: https://www.camera.it/leg19/995?sezione=documenti&tipoDoc=lavori_testo_pdl&idLegislatura=19&codice=leg.19.pdl.camera.2095.19PDL0111760&back_to=
  • Testo Proposta di legge N. 2220. Available at: https://www.camera.it/leg19/995?sezione=documenti&tipoDoc=lavori_testo_pdl&idLegislatura=19&codice=leg.19.pdl.camera.2220.19PDL0126920&back_to=
  • Testo Proposta di legge N. 2220. Available at: https://www.camera.it/leg19/995?sezione=documenti&tipoDoc=lavori_testo_pdl&idLegislatura=19&codice=leg.19.pdl.camera.2220.19PDL0126920&back_to=
  • Testo unificato elaborato dal comitato ristretto, adottato come testo base dalla commissione. Disposizioni in materia di terapie digitali. C. 1208 Loizzo, C. 2095 Quartini e C. 2220 Girelli. Available at: https://www.camera.it/leg19/995?sezione=documenti&tipoDoc=lavori_testo_pdl&idLegislatura=19&codice=leg.19.pdl.camera.1208.19PDLTU12080&back_to=
  • Terapie digitali - Audizioni - Agenzia Italia digitale, Federated Innovation @Mind - Società italiana farmacia ospedaliera e servizi farmaceutici Aziende sanitarie (SIFO). Martedì 19 Novembre 2024 ore 13:00. Available at: https://webtv.camera.it/evento/26681
  • Terapie digitali - Audizione - FOFI; FNOPI. Mercoledì 29 Gennaio 2025 ore 14:50. Available at: https://webtv.camera.it/evento/27179
  • Terapie digitali - Audizione - Francesco Saverio Mennini e Achille Iachino, Ministero salute; Silvio Garattini, Istituto Mario Negri, FNOMCeO. Mercoledì 05 Febbraio 2025 ore 15:00. Available at: https://webtv.camera.it/evento/27243
  • Terapie digitali - Audizione - Giulio Siccardi, direttore generale f.f. AGENAS. Mercoledì 12 Febbraio 2025 ore 15:20. Available at: https://webtv.camera.it/evento/27349
  • Quotidiano Sanità. In arrivo il Programma Nazionale Hta Dispositivi Medici 2026-2028: innovazione, evidenze e governance condivisa. 14 ottobre 2025. Available at: https://www.quotidianosanita.it/governo-e-parlamento/articolo.php?articolo_id=132634
  • Terapie digitali - Audizione - Robert Giovanni Nisticò, presidente AIFA. Mercoledì 19 Febbraio 2025 ore 15:00. Available at: https://webtv.camera.it/evento/27416
  • American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.).
  • Thapar, A., Cooper, M., Rutter, M. (2017). Neurodevelopmental disorders. The Lancet Psychiatry, 4(4), 339–346.
  • Estes, A., Munson, J., Rogers, S., Greenson, J., Winter, J., & Dawson, G. (2015). Long-term outcomes of early intervention in 6‑year‑old children with autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 54(7), 580–587.
  • Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of digital therapeutics. npj Digital Medicine, 3, 12.
  • De Angelis C, Bunker S, Schoo A. Exploring the barriers and enablers to attendance at rural cardiac rehabilitation programs. The Australian Journal of Rural Health, 2008,16:137-142.
  • Insel, T. R., & Quir, B. (2017). Revolutionizing mental health with digital therapeutics. JAMA Psychiatry, 74(1), 1–2
  • Smolen P, Zhang Y, Byrne JH. The right time to learn: mechanisms and optimization of spaced learning. Nat Rev Neurosci. 2016 Feb;17(2):77-88.
  • Espie CA, Firth J, Torous J. Evidence-informed is not enough: digital therapeutics also need to be evidence-based. World Psychiatry. 2022 Jun;21(2):320-321.
  • Kumar A, Ross JS, Patel NA, Rathi V, Redberg RF, Dhruva SS. Studies Of Prescription Digital Therapeutics Often Lack Rigor And Inclusivity. Health Aff (Millwood). 2023 Nov;42(11):1559-1567.
  • Phan P, Mitragotri S, Zhao Z. Digital therapeutics in the clinic. Bioeng Transl Med. 2023 May 3;8(4):e10536.
  • Lee NK, Kim JS. Status and Trends of the Digital Healthcare Industry. Healthc Inform Res. 2024 Jul;30(3):172-183.
  • Lord, C., Charman, T., Havdahl, A., Carper, M., & D'Angelo, D. (2020). The Lancet Commission on the future of care and clinical research in autism. The Lancet, 7, 85–107.
  • Torous J., Firth J., Huckvale K., Larsen M. E., Hsin H., Campbell I. (2017). The Emerging Role of Digital Phenotyping in Psychiatry and Mental Health. JAMA Psychiatry, 74(4):307-308.
  • Marino, L., et al. (2022). Limitations of digital interventions in neurodevelopmental disorders: a systematic review. Journal of Autism and Developmental Disorders, 52, 3852–3867.
  • Chustecki M. Benefits and Risks of AI in Health Care: Narrative Review. Interact J Med Res. 2024 Nov 18;13:e53616.
  • Van Dyck, L., et al. (2023). Digital divide in access to digital therapeutics among families of children with disabilities. Journal of Medical Internet Research, 25, e45678.
  • Mannheim I, Köttl H. Ageism and (Successful) Digital Engagement: A Proposed Theoretical Model. Gerontologist. 2024 Sep 1;64(9):gnae078.
  • Rosales, A., & Fernández-Ardèvol, M. (2020). Ageism in the era of digital platforms. Convergence, 26(5–6), 1074–1087.
  • Shonkoff, J. P., et al. (2021). The Limits of Digital Interventions in Early Childhood. Pediatrics, 147(5), e20200438.
  • Sucala M, Schnur JB, Constantino MJ, Miller SJ, Brackman EH, Montgomery GH. The therapeutic relationship in e-therapy for mental health: a systematic review. J Med Internet Res. 2012 Aug 2;14(4):e110.
  • Whitehouse, A. J. O., et al. (2022). Transparent machine learning for autism diagnosis: advances and pitfalls. NPJ Digital Medicine, 5, 67.
  • Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Ment Health. 2021 Jun 10;8(6):e24668.
  • European Commission. (2020). Ethics guidelines for trustworthy AI.
  • Cavazos-Rehg P, Min C, Fitzsimmons-Craft EE, Savoy B, Kaiser N, Riordan R, Krauss M, Costello S, Wilfley D. Parental consent: A potential barrier for underage teens' participation in an mHealth mental health intervention. Internet Interv. 2020 May 20;21:100328.
  • Barton, A., Kim, D., & Singh, H. (2021). Algorithmic bias detection and mitigation in clinical AI systems. Annual Review of Biomedical Data Science, 4, 123–144.
  • Wagner, J., et al. (2023). Ensuring equitable access to digital health for children with neurodevelopmental conditions. Child and Adolescent Psychiatric Clinics, 32(2), 197–212.
  • Char, D. S., Shah, N. H., & Magnus, D. (2020). Implementing machine learning in health care — addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983.
  • Gillon R. Medical ethics: four principles plus attention to scope. BMJ. 1994 Jul 16;309(6948):184-8.
  • Clay RA. Mental health apps are gaining traction. 2021. Available at https://www.apa.org/monitor/2021/01/trends-mental-health-apps
  • Lecomte T, Potvin S, Corbière M, Guay S, Samson C, Cloutier B, et al.Mobile apps for mental health issues: meta-review of meta-analyses. JMIR Mhealth Uhealth 2020;8:e17458.
  • Chandrashekar P. Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps. Mhealth 2018;4:6.
  • Efficacy of Edupression.Com® in Depressive Patients (eFICASY). NCT04839822. Available at: https://clinicaltrials.gov/ct2/show/NCT04839822
  • Lorenz, Noah; Heim, Eva; Roetger, Alexander; Birrer, Eva; Maercker, Andreas (2019). Randomized controlled Trial to Test the Efficacy of an Unguided Online Intervention with Automated Feedback for the Treatment of Insomnia. Behavioural and Cognitive Psychotherapy, 47(3):287-302.
  • Bundesinstitut für Arzneimittel und Medizinprodukte website. Access to the Verzeichnis | DiGA-Verzeichnis. Available at: https://diga.bfarm.de/de/verzeichnis
  • Cao Z, Zhang J, Lu Z, Chen H, Min J, Hou Y, Wang X, Xu C. Physical Activity, Mental Activity, and Risk of Incident Stroke: A Prospective Cohort Study. Stroke. 2024 May;55(5):1278-1287. doi: 10.1161/STROKEAHA.123.044322. Epub 2024 Mar 27. PMID: 38533647.
  • McNamara A, Baetu I, Collins-Praino L. History of Traumatic Brain Injury Does Not Influence Rate of Progression of Clinical or Pathological Outcomes in Two Early Parkinson's Disease Cohorts. Eur J Neurol. 2025 Mar;32(3):e70090. doi: 10.1111/ene.70090. PMID: 40114418; PMCID: PMC11926254.
  • Groun N, Villalba-Orero M, Casado-Martín L, Lara-Pezzi E, Valero E, Le Clainche S, Garicano-Mena J. Eigenhearts: Cardiac diseases classification using eigenfaces approach. Comput Biol Med. 2025 Jun;192(Pt A):110167. doi: 10.1016/j.compbiomed.2025.110167. Epub 2025 Apr 26. PMID: 40288290.
  • Laurens van der Maaten, Geoffrey Hinton. Visualizing Data using t-SNE. 9(86):2579−2605, 2008.
  • Kobak D, Berens P. The art of using t-SNE for single-cell transcriptomics. Nat Commun. 2019 Nov 28;10(1):5416. doi: 10.1038/s41467-019-13056-x. PMID: 31780648; PMCID: PMC6882829.
  • Huff DT, Weisman AJ, Jeraj R. Interpretation and visualization techniques for deep learning models in medical imaging. Phys Med Biol. 2021 Feb 2;66(4):04TR01. doi: 10.1088/1361-6560/abcd17. PMID: 33227719; PMCID: PMC8236074.
  • Magnuska ZA, Roy R, Palmowski M, Kohlen M, Winkler BS, Pfeil T, Boor P, Schulz V, Krauss K, Stickeler E, Kiessling F. Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images. Radiology. 2024 Sep;312(3):e232554. doi: 10.1148/radiol.232554. PMID: 39254446.
  • Li D, Shukla A, Chandaka S, Taylor B, Xu J, Liu M. Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study. JMIR Med Inform. 2025 Jul 24;13:e68830. doi: 10.2196/68830. PMID: 40706557; PMCID: PMC12289314.
  • Liu M, Li S, Yuan H, Ong MEH, Ning Y, Xie F, Saffari SE, Shang Y, Volovici V, Chakraborty B, Liu N. Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artif Intell Med. 2023 Aug;142:102587. doi: 10.1016/j.artmed.2023.102587. Epub 2023 May 22. PMID: 37316097.
  • Nisa Aulia Saputra, Lala Septem Riza, Agus Setiawan, Ida Hamidah. A Systematic Review for Classification and Selection of Deep Learning Methods. Decision Analytics Journal. Vol. 12, Sep 2024. https://doi.org/10.1016/j.dajour.2024.100489
  • Ghalavand H, Shirshahi S, Rahimi A, Zarrinabadi Z, Amani F. Common data quality elements for health information systems: a systematic review. BMC Med Inform Decis Mak. 2024 Sep 2;24(1):243. doi: 10.1186/s12911-024-02644-7. PMID: 39223578; PMCID: PMC11367888.
  • Bailly A, Blanc C, Francis É, Guillotin T, Jamal F, Wakim B, Roy P. Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models. Comput Methods Programs Biomed. 2022 Jan;213:106504. doi: 10.1016/j.cmpb.2021.106504. Epub 2021 Oct 28. PMID: 34798408.
  • Pezoulas VC, Zaridis DI, Mylona E, Androutsos C, Apostolidis K, Tachos NS, Fotiadis DI. Synthetic data generation methods in healthcare: A review on open-source tools and methods. Comput Struct Biotechnol J. 2024 Jul 9;23:2892-2910. doi: 10.1016/j.csbj.2024.07.005. PMID: 39108677; PMCID: PMC11301073.
  • Software as a Medical Device (SaMD): Key Definitions. Technical document. International Medical Device Regulators Forum. IMDRF Code: IMDRF/SaMD WG/N10. Published date: 18 December 2013. Available at: https://www.imdrf.org/documents/software-medical-device-samd-key-definitions
  • Artificial Intelligence in Software as a Medical Device. FDA. Available at: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device
  • Moritz M, Topol E, Rajpurkar P. Coordinated AI agents for advancing healthcare. Nat Biomed Eng. 2025 Apr;9(4):432-438. doi: 10.1038/s41551-025-01363-2. PMID: 40169759.
  • Singh R, Bapna M, Diab AR, Ruiz ES, Lotter W. How AI is used in FDA-authorized medical devices: a taxonomy across 1,016 authorizations. NPJ Digit Med. 2025 Jul 1;8(1):388. doi: 10.1038/s41746-025-01800-1. PMID: 40596700; PMCID: PMC12219150.
  • MDR Classification Rule 11: The classification nightmare? Johner Institute. June 25, 2025. Available at: https://blog.johner-institute.com/regulatory-affairs/mdr-rule-11/
  • Yixing Jiang, Kameron C. Black, D.O., M.P.H., Gloria Geng, Danny Park, James Zou, Ph.D., Andrew Y. Ng, Ph.D., and Jonathan H. Chen, M.D., Ph.D. MedAgentBench: A Virtual EHR Environment to Benchmark Medical LLM Agents. Published August 14, 2025. NEJM AI 2025;2(9). DOI: 10.1056/AIdbp250014
  • Medical Devices. Joint Artificial Intelligence Board and Medical Device Coordination Group Document. AIB 2025-1. MDCG 2025-6. Interplay between the Medical Devices Regulation (MDR) & In vitro Diagnostic Medical Devices Regulation (IVDR) and the Artificial Intelligence Act (AIA). June 2025. Available at: https://health.ec.europa.eu/document/download/b78a17d7-e3cd-4943-851d-e02a2f22bbb4_en?filename=mdcg_2025-6_en.pdf
  • Cabitza F, Parimbelli E. Let XAI generate reliability metadata, not medical explanations. Comput Methods Programs Biomed. 2026 Jan;273:109090. doi: 10.1016/j.cmpb.2025.109090. Epub 2025 Oct 8. PMID: 41072128.
  • Cabitza F, Parimbelli E. Let XAI generate reliability metadata, not medical explanations. Comput Methods Programs Biomed. 2026 Jan;273:109090. doi: 10.1016/j.cmpb.2025.109090. Epub 2025 Oct 8. PMID: 41072128.
  • Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, Jeffrey Jopling, Michael Moor. AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments. 2025. arXiv:2405.07960
  • de Koning, H. J. et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N. Engl. J. Med. 382, 503–513 (2020).
  • Knudsen, A. B. et al. Estimated US cancer deaths prevented with increased use of lung, colorectal, breast, and cervical cancer screening. JAMA Netw. Open 6, e2344698 (2023).
  • Crosby, D. et al. Early detection of cancer. Science 375, (2022).
  • Hernström V, Josefsson V, et al. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI), Lancet Digit Health. 2025 Mar;7(3):e175-e183
  • Karin Dembrower, MD, ∙ Alessio Crippa, PhD, Eugenia Colón, MD, Prof Martin Eklund, PhD, Fredrik Strand, MD and the ScreenTrustCAD Trial Consortium. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. The Lancet. Volume 5, Issue 10, E703-E711, October 2023. 10.1016/S2589-7500(23)00153-X
  • Dembrower KE, Crippa A, Eklund M, Strand F. Human-AI Interaction in the ScreenTrustCAD Trial: Recall Proportion and Positive Predictive Value Related to Screening Mammograms Flagged by AI CAD versus a Human Reader. Radiology. 2025 Mar;314(3):e242566. doi: 10.1148/radiol.242566. PMID: 40100021.
  • Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
  • Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).
  • Shamai, G. et al. Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer. Nat. Commun. 13, 6753 (2022).
  • Sun, D., Wang, M. & Li, A. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans. Comput. Biol. Bioinform. 16, 841–850 (2018).
  • Volinsky-Fremond, S. et al. Prediction of recurrence risk in endometrial cancer with multimodal deep learning. Nat. Med. 30, 1962–1973 (2024).
  • Prelaj A, Ganzinelli M, Trovo' F, et al. The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care with Immunotherapy. Clin Lung Cancer. 2023;24(4):381-387. doi: 10.1016/j.cllc.2023.02.005
  • Prelaj, Arsela, et al. "Apollo 11 project, consortium in advanced lung cancer patients treated with innovative therapies: integration of real-world data and translational research." Clinical Lung Cancer 25.2 (2024): 190-195.
  • Unger, J. M., Shulman, L. N., Facktor, M. A., Nelson, H. & Fleury, M. E. National estimates of the participation of patients with cancer in clinical research studies based on Commission on Cancer accreditation data. J. Clin. Oncol. 42, 2139–2142 (2024).
  • Murthy, V. H., Krumholz, H. M. & Gross, C. P. Participation in cancer clinical trials: race-, sex-, and age-based disparities. JAMA 291, 2720–2726 (2004).
  • Kammula, A. V., Schäffer, A. A., Rajagopal, P. S., Kurzrock, R. & Ruppin, E. Outcome differences by sex in oncology clinical trials. Nat. Commun. 15, 2608 (2024).
  • Ferber, D., El Nahhas, O.S.M., Wölflein, G. et al. Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology. Nat Cancer 6, 1337–1349 (2025). https://doi.org/10.1038/s43018-025-00991-6
  • Page, M.J., et al., The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 2021. 372: p. n71.
  • Davison, N.J., et al., Cost-Effectiveness Analysis of a Prescription Digital Therapeutic in Type 2 Diabetes. Adv Ther, 2024. 41(2): p. 806–825.
  • Nomura, A., et al., Cost-effectiveness of digital therapeutics for essential hypertension. Hypertens Res, 2022. 45(10): p. 1538–1548.
  • Lewkowicz, D., E. Bottinger, and M. Siegel, Economic Evaluation of Digital Therapeutic Care Apps for Unsupervised Treatment of Low Back Pain: Monte Carlo Simulation. JMIR Mhealth Uhealth, 2023. 11: p. e44585.
  • Lewkowicz, D., A.M. Wohlbrandt, and E. Bottinger, Digital Therapeutic Care Apps With Decision-Support Interventions for People With Low Back Pain in Germany: Cost-Effectiveness Analysis. JMIR Mhealth Uhealth, 2022. 10(2): p. e35042.
  • Cloosterman, S., et al., The Potential Impact of Digital Biomarkers in Multiple Sclerosis in The Netherlands: An Early Health Technology Assessment of MS Sherpa. Brain Sci, 2021. 11(10).
  • Koc, I., et al., Cost-effectiveness Analysis of Digital Therapeutics for Amblyopia. Ophthalmology, 2025. 132(6): p. 654–660.
  • Velez, F.F., et al., Five-year budget impact of a prescription digital therapeutic for patients with opioid use disorder. Expert Rev Pharmacoecon Outcomes Res, 2022. 22(4): p. 599–607.
  • Velez, F.F. and D.C. Malone, Cost-Effectiveness Analysis of a Prescription Digital Therapeutic for the Treatment of Opioid Use Disorder. J Mark Access Health Policy, 2021. 9(1): p. 1966187.
  • Velez, F.F., et al., Evaluation of the cost-utility of a prescription digital therapeutic for the treatment of opioid use disorder. Postgrad Med, 2021. 133(4): p. 421–427.
  • Liu, T., et al., Cost-effectiveness analysis of digital therapeutics for home-based cardiac rehabilitation for patients with atrial fibrillation after catheter ablation. Digit Health, 2023. 9: p. 20552076231211548.
  • Wang, W., et al., Economic modeling of reSET-O, a prescription digital therapeutic for patients with opioid use disorder. J Med Econ, 2021. 24(1): p. 61–68.
  • Liu, T., et al., Cost-effectiveness analysis of digital therapeutics for home-based cardiac rehabilitation for patients with chronic heart failure: model development and data analysis. Cost Eff Resour Alloc, 2023. 21(1): p. 82.
  • Nordyke, R.J., K. Appelbaum, and M.A. Berman, Estimating the Impact of Novel Digital Therapeutics in Type 2 Diabetes and Hypertension: Health Economic Analysis. J Med Internet Res, 2019. 21(10): p. e15814.