{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T09:25:00Z","timestamp":1786958700158,"version":"build-2736575974"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment\u2014intrinsically subject to inter-observer and intra-observer variability\u2014with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework\u2014supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector.<\/jats:p>","DOI":"10.3390\/life16081346","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T08:05:41Z","timestamp":1786953941000},"page":"1346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3797-0507","authenticated-orcid":false,"given":"Carmela","family":"Baviello","sequence":"first","affiliation":[{"name":"Research and Training Center for Health and Well-Being, University Guglielmo Marconi, via Plinio 44, 00193 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela Maria","family":"Capuano","sequence":"additional","affiliation":[{"name":"Research and Training Center for Health and Well-Being, University Guglielmo Marconi, via Plinio 44, 00193 Roma, Italy"},{"name":"Academy for Health and Clinical Research, University Guglielmo Marconi, via Plinio 44, 00193 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1869-3912","authenticated-orcid":false,"given":"Roberto","family":"Verna","sequence":"additional","affiliation":[{"name":"Research and Training Center for Health and Well-Being, University Guglielmo Marconi, via Plinio 44, 00193 Roma, Italy"},{"name":"Academy for Health and Clinical Research, University Guglielmo Marconi, via Plinio 44, 00193 Roma, Italy"},{"name":"World Association of Societies of Pathology and Laboratory Medicine, University Guglielmo Marconi, via Plinio 44, 00193 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1001\/jama.2015.1405","article-title":"Diagnostic concordance among pathologists interpreting breast biopsy specimens","volume":"313","author":"Elmore","year":"2015","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e407","DOI":"10.1016\/S2589-7500(20)30159-X","article-title":"An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: A blinded clinical validation and deployment study","volume":"2","author":"Pantanowitz","year":"2020","journal-title":"Lancet Digit. 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