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Recent technological innovations, particularly the incorporation of real-time magnetic resonance imaging (MRI) with linear accelerators, have markedly enhanced RT precision, improving target coverage and reducing radiation exposure to surrounding healthy tissues. Furthermore, real-time MRI enables the collection of quantitative imaging data during each treatment fraction, potentially leading to the identification of quantitative imaging biomarkers. These biomarkers can capture dynamic biological changes during RT, offering unprecedented insights into treatment response. The integration of these imaging biomarkers with clinical, genomic, and pathological data into artificial intelligence (AI)-supported clinical decision support systems promises to further refine therapeutic personalisation. In this context, AI plays a central role by automating labour-intensive tasks, extracting quantitative metrics, and integrating multidimensional data into clinically meaningful predictive models. This review outlines a vision for the future of RT, highlighting how the synergy of advanced imaging, AI, and multidomain data through three logical steps: (1) rethinking and reorganising the patient care journey; (2) from imaging \u201cfor\u201d to imaging \u201cwith\u201d RT; and (3) incorporation into clinical decision support systems. This integration will support the development of personalised, biologically driven treatment strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>The longitudinal integration of diagnostic imaging and RT, facilitated by AI, could significantly enhance clinical workflow efficiency and therapeutic accuracy in oncology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Oncological care is transitioning from disease-centred to patient-centred, with tumour boards representing the junction for shared multidisciplinary decisions.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Integrating advanced imaging with RT enables quantitative imaging biomarkers extraction that captures tumour changes throughout the course of treatment.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Artificial intelligence plays a central role in automating resource-intensive processes and integrating large-scale multidomain data towards personalised medicine.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-025-00664-0","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T09:22:18Z","timestamp":1766395338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Personalised medicine through AI-enhanced integration of diagnostic imaging and radiation therapy"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7594-898X","authenticated-orcid":false,"given":"Silvia","family":"Bottazzi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuditta","family":"Chiloiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Russo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Rame","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandra","family":"Iacono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benedetta","family":"Gui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Boldrini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Antonietta","family":"Gambacorta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evis","family":"Sala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"664_CR1","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1016\/j.clon.2024.05.018","volume":"36","author":"DJ Noble","year":"2024","unstructured":"Noble DJ, Ramaesh R, Brothwell M et al (2024) The evolving role of novel imaging techniques for radiotherapy planning. 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