https://www.journaljamps.com/index.php/JAMPS/issue/feed 2026-09-12T12:47:43+00:00 Journal of Advances in Medical and Pharmaceutical Sciences [email protected] Open Journal Systems <p style="text-align: justify;"><strong>Journal of Advances in Medical and Pharmaceutical Sciences (ISSN:&nbsp;2394-1111)</strong>&nbsp;aims to publish high quality papers (<a href="/index.php/JAMPS/general-guideline-for-authors">Click here for Types of paper</a>) in all areas of&nbsp;Medical and Pharmaceutical Sciences.&nbsp;By not excluding papers based on novelty, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer-reviewed, open-access INTERNATIONAL journal.</p> https://www.journaljamps.com/index.php/JAMPS/article/view/893 2026-09-12T12:47:17+00:00 Emmanuel Niiboye Odai [email protected] Musa Shamaki Ibrahim Mable Nalugya David Tetteh Akuaku Blemano Gideon Owusu Nurudeen Gbadegesin

<p><strong>Aims:</strong> To synthesise and critically appraise evidence on artificial intelligence (AI) and multimodal data approaches for diagnosis, prognosis, and treatment decision support in diffuse large B-cell lymphoma (DLBCL) and acute myeloid leukaemia (AML), and to compare how disease biology shapes model design and clinical readiness.</p> <p><strong>Study Design:</strong> Systematic review with narrative synthesis.</p> <p><strong>Place and Duration of Study:</strong> PubMed/MEDLINE, Embase, Scopus, Web of Science Core Collection, IEEE Xplore, and the Cochrane Library were searched from database inception to 29 July 2026.</p> <p><strong>Methodology:</strong> English-language original studies involving human participants, clinical images, human-derived samples, or patient-derived datasets were eligible. Two reviewers independently screened records, assessed full texts, and extracted study characteristics, data modalities, analytical methods, validation approaches, and performance measures. PROBAST+AI domains were used to guide appraisal of eligible patient-level prediction models; exploratory multi-omics, molecular-subtyping, biomarker-discovery, and segmentation studies underwent structured descriptive appraisal. TRIPOD+AI informed assessment of reporting completeness. Owing to substantial clinical and methodological heterogeneity, findings were synthesised narratively.</p> <p><strong>Results:</strong> Twenty-eight studies were included: 22 on DLBCL and six on AML. DLBCL research was dominated by PET/CT radiomics, deep imaging, digital pathology, and imaging-clinical-molecular fusion. Internally evaluated diagnostic models reported area under the curve values as high as 0.999, whereas externally validated prognostic models achieved values of 0.66-0.71 and showed inconsistent incremental improvement over the International Prognostic Index. AML studies mainly integrated genomic, transcriptomic, epigenomic, single-cell, proteomic, metabolic, and functional drug-response data. Prognostic models reported concordance indices of 0.72-0.81 and time-dependent area under the curve values of 0.795-0.899. The most frequent methodological concerns were retrospective sampling, high-dimensional modelling in modest cohorts, incomplete calibration reporting, and limited independent validation. Treatment-response models in both diseases remained retrospective or exploratory, and none demonstrated improved outcomes through AI-guided treatment allocation.</p> <p><strong>Conclusion:</strong> AI and multimodal data approaches show greatest maturity for prognostic stratification, but diagnostic replacement and treatment selection remain unproven. Prospective, multicentre clinical-impact validation is the principal requirement for translation into routine care.</p>

2026-09-12T00:00:00+00:00 Copyright (c) 2026 Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.