Recent literature from 2025 to 2026 has firmly established AI as a core driver in the methodological evolution of precision oncology for HCC. By implementing VFMs to mitigate imaging domain shifts and

The gap

Consensus across the literature

Supporting evidence — 7 representative gaps

• Systematic review of pituitary gland and pituitary adenoma automatic segmentation techniques in magnetic resonance imaging (2026) · doi

  increasingly high, methodological and reporting currently hinder into routine clinical practice. Addressing these gaps—particularly in dataset diversity, protocol transparency, and validation strategy—represents a necessary step toward reliable clinical deployment. Collectively, these challenges emphasize the need for larger, diverse, and well-annotated datasets; standardized reporting of PG & PA features; inclusion of multimodal clinical data; and external validation. As segmentation models advance, these methodological improvements will be critical to ensuring their reliability, generalizability, and eventual clinical adoption. 4.2.1 Study limitations In this systematic review, a noteworthy limitation is the time interval between the last database search (October 2024) and the completion of this manuscript (July 2025). Given the rapid pace of developments in medical image segmentation and deep learning, it is possible that additional studies have since been published that further advance the field. In addition, a quantitative meta-analysis was not feasible due to the heterogeneity and limitations in the reporting of MRI acquisition parameters, dataset characteristics, and PG & PA features. 4.3 Future directions Future work should focus on developing generalisable models for PG and PA segmentation across diverse imaging protocols and populations, supported by large, multi-institutional annotated datasets. Integrating multimodal information—such as clinical or endocrine markers—may enhance diagnostic relevance and bridge the gap between imaging and functional assessment. Standardised benchmarking frameworks and rigorous external fair comparison across to enable validation are essential methods and to support clinical translation. From a clinical implementation perspective, future studies should be designed as prospective or retrospective clinical validation studies that explicitly account for the methodological limitations identified in this review, including variability in imaging protocols, limited reporting of acquisition parameters, and tumour characteristics. Addressing these factors within study design would facilitate more robust evaluation of model reliability and generalisability. inconsistent documentation of patient and

  Keywords: clinical reporting validation methodological segmentation limitations future imaging addressing dataset diverse annotated datasets features multimodal
• Development and validation of a nomogram using interpretable machine learning to integrate CT radiomics and PET metabolic parameters for predicting benign-malignant differentiation of pulmonary space-occupying lesions (2026) · doi

  Frontiers in Radiology 14 frontiersin.org Liu et al. 10.3389/fradi.2026.1782678 to optimize the workflow. Third, different scanners, protocols, and patient populations remains to be verified. We will conduct multi-center external validation with standardized imaging harmonization strategies in future work. Second, the retrospective design may introduce selection bias, and the sample size may insufficiently capture the heterogeneity of rare PSOL subtypes. Meanwhile, manual segmentation, despite excellent reproducibility, is labor-intensive for large-scale application; we will explore automated segmentation algorithms in larger multi-center cohorts the biological mechanisms underlying the selected radiomic features require further radiogenomic validation. Additionally, SHAP analysis can only explain the model’s decision logic and feature contributions, but cannot infer direct biological causality between features and PSOL malignant behavior, which warrants further pathological and radiogenomic verification. Finally, we did not perform systematic pathological subtype stratification for benign and malignant PSOLs. Given the significant radiomic and metabolic heterogeneity across different pathological subtypes, we will conduct subtype-specific subanalysis further optimize the model’s robustness and diagnostic performance. in the subsequent multi-center study to

  Keywords: multi center further pathological optimize different veri conduct validation heterogeneity psol subtypes segmentation biological radiomic
• Application of artificial intelligence in paediatric oncology imaging (2026) · doi

  Radiologists, as well as everyone involved with the use of AI in a clinical setting, require familiarity with AI systems to leverage them safely and effectively in clinical practice. AI excels at processing large datasets and handling repeti- tive tasks, while radiologists provide judgment, context, and adaptability for complex cases, as well as the essential task of data curation. Combining these strengths can lead to more accurate, efficient, and reliable medical imaging practices, accelerating the translation of AI models into clinical prac- tice [12, 13, 83, 90]. Overcoming the limitations of paediatric oncology data The challenges of AI development in paediatric oncology imaging stem from limited data availability and the time- consuming, labour-intensive process of producing anno- tations, problems exacerbated by the need for datasets diverse enough to represent the entire paediatric age spec- trum. These challenges can be addressed in several ways: increasing the availability of data, applying (AI) methods that generate synthetic data or learn effectively from limited examples, or combining both strategies in a complementary manner. A primary approach to expanding data availability involves multi-institutional collaboration, which can help create datasets that are more diverse across age, demo- graphics, and clinical characteristics. These initiatives rely on standardised protocols and ethical agreements to ensure privacy, reproducibility, and harmonisation [12]. Further- more, documenting datasets with “datasheets” that detail their composition, collection, and intended uses is critical for responsible implementation [93]. Several dedicated initiatives and platforms now support sharing of paediatric imaging data. Examples of open data- sets and online platforms offering downloadable data across a range of disease types, including oncology, are provided in Table 2 and Table 3. These resources can represent a sub- stantial source of labelled and unlabelled data for diverse AI training purposes. Text-based initiatives like the Pediatric Cancer Data Commons [97], which integrate clinical data across tumour types, are particularly valuable. Their impact could be strengthened by coupling them with dedicated imaging databases, creating larger and more diverse datasets for robust AI development. Such paediatric resources are urgently needed, given that large, dedicated imaging datasets have already become indispensable tools for advancing AI research in adult oncology [64, 89, 100]. Federated infrastructures, furthermore, enable collabo- rative AI development across institutions without sharing sensitive patient data, preserving privacy while leveraging heterogeneous, decentralised datasets. This decentralised approach allows models to be trained locally within each participating institution, with only aggregated model updates exchanged across sites [9, 12]. Platforms like the Europe

  Keywords: datasets clinical imaging paediatric across oncology diverse development availability initiatives dedicated platforms radiologists well them
• Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer (2026) · doi

  In conclusion, we have summarized the recent advancements in application of AI-based approaches for screening, diagnosis, prognosis and treatment of CC. A visual summary of these efforts is depicted in Figure 5. AI algorithms, particularly ML and DL, have played a significant role in transforming CC screening, diagnosis, prognosis and treatment by outperforming human experts. AI-powered tools can analyze digitalized cytological, histopathological, colposcopic images to detect abnormal cells or lesions and contribute to fast, accurate and early detection of CC. AI-based prognostic models, particularly DL models, integrated clinical, histopathological, radiomic data to predict LNM, treatment response, survival outcome, and post-operative risk factors, thereby contributing to better patient outcomes. By analyzing advanced radiological images and treatment plans, AI- based models are transforming CC treatment by segmentation of CTV, OAR, dose prediction and treatment planning. and patient treatment accelerates thorough evaluation facilitates a accuracy improving The integration of multimodal data sets, including clinical variables, imaging, genomic, proteomic, and patient-reported that enhances outcomes, diagnostic initiation, of ultimately complementary information from different data sources improves diagnostic accuracy, risk stratification and personalized treatment recommendations. Collaboration across multiple centers and institutions is required to generate large, high-quality and diverse datasets for training, testing, validating and generalizability of the AI models. The advancement of explainable and transparent AI is essential for comprehending the decision-making processes of AI

  Keywords: treatment models based patient screening diagnosis prognosis particularly transforming histopathological images clinical risk outcomes accuracy
• Artificial Intelligence in Diagnostic Pathology: A Comprehensive Review of Current Applications and Future Prospects (2026) · doi

  As artificial intelligence advances, its potential to change diagnostic pathology grows even more attractive. Future AI breakthroughs are predicted to transform the integration of morphological data with other types of clinical information, resulting in more tailored [130]. Furthermore, new machine learning approaches will provide more dynamic, flexible, and secure solutions to treatment options and precise Copyright: © Author(s), 2026. Published by Greenfort International Journal of Applied Medical Science | This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. 174 Belukurichi Sadasivam Sangeetha Tripathi et al.; Grn Int J Apl Med Sci, May-Jun, 2026; 4(3):167-181 improve diagnostic accuracy, workflow efficiency, and cross-institutional collaboration [131]. in order 7.1 Precision and Predictive Pathology One of the most important future uses of AI in pathology is precision medicine [63]. AI will make it easier to integrate different sorts of data, such as morphological traits, genetic data, proteomic data, and clinical results, to provide personalized treatment plans tailored to each patient's specific needs. By merging these data sources, AI algorithms will improve their ability to forecast disease development, find biomarkers for targeted therapy, and optimise treatment plans based on an individual's genetic profile [132]. This data-driven strategy promises to transform healthcare from a "one-size-fits-all" model to highly individualized care, resulting improved patient outcomes and fewer adverse responses to medicines [133]. in 7.2 Self-Learning and Adaptive Models Future AI models are anticipated to include self- learning and adaptive learning capabilities, allowing them to continuously improve as they process new data. Most AI systems are now static and require manual retraining with new datasets [134]. Continuous learning algorithms, which can adjust to new data in real time, are expected to produce more resilient and up-to-date diagnostic tools. These models will adapt in response to new clinical data, allowing them to recognize emerging illness patterns and increase diagnostic accuracy over time [135]. This adaptive technique reduces the need for periodic retraining, making AI solutions more adaptable and better suited to the fast-paced nature of medical developments [136]. in healthcare. This 7.3 Federated Learning Federated learning is an important advancement in AI training, especially technique trained across various enables AI models to be institutions and datasets without sharing sensitive patient data, hence protecting patient privacy.

  Keywords: learning diagnostic patient models pathology future clinical treatment improve adaptive transform morphological resulting tailored provide
• The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility (2026) · doi

  Recent literature from 2025 to 2026 has firmly established AI as a core driver in the methodological evolution of precision oncology for HCC. By implementing VFMs to mitigate imaging domain shifts and deploying spatial deconvolution algorithms to decode metabolic interactions within the TME, AI has advanced beyond superficial pattern recognition to deep, mechanistic feature extrac- tion. Nevertheless, objective evidence demonstrating that tradi- tional statistical models continue to outperform complex LLMs Looking ahead, medical AI must move beyond an overreliance on “post hoc explainability” tools, such as SHAP or LIME, recog- nizing that their reliability fluctuates dynamically with data com- plexity and application scenarios. Although these post hoc attribution methods excel at highlighting correlative patterns, they face significant limitations in establishing true causality, are gener- ally not engineered to establish biological causality, and may exhibit pronounced mathematical instability within highly complex non- linear networks. Future AI applications in hepatology must TABLE 5 Multimodal integration and multi-omics AI applications in HCC.

  Keywords: within beyond complex must post causality applications recent literature rmly established core driver methodological evolution
• Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer (2026) · doi

  Data remains the paramount and essential element for the education of AI systems. Utilizing contemporary information processing technologies to exploit radiology report databases may enhance report search and retrieval, thereby assisting radiologists in diagnosis. There is a necessity to advocate for the establishment of interconnected networks that identify patient data globally and facilitate large-scale AI training tailored to diverse patient demo- graphics, geographic regions, and diseases. Furthermore, we under- score the necessity for more diversified imaging libraries for uncommon malignancies, including OC. However, much of the literature is limited by common methodological current weaknesses, including retrospective study designs, small cohort sizes, lack of prospective validation, spectrum bias, variable label quality, segmentation variability, scanner and protocol heterogene- ity, risk of data leakage, and inconsistent reporting of model calibration and clinical utility. These limitations highlight the need for more rigorous and standardized study designs to improve the reliability and generalizability of AI applications in oncology. In image-based diagnostic tasks, AI models have demonstrated performance comparable to, or exceeding, that of expert physicians. However, such evaluations often fail to account for the multidi- mensional information routinely considered by radiologists when interpreting complex examinations. Non-imaging patient attri- butes, including demographic characteristics, clinical history, and genetic or molecular data, provide critical contextual information that is not fully captured by image-only models and can substan- tially enhance predictive performance when appropriately inte- grated (90). The high performance of contemporary AI models is frequently accompanied by substantial algorithmic complexity, involving high-dimensional feature spaces and deep neural network architectures. As a result, the internal decision-making processes underlying image-based predictions are often difficult to interpret or explain, a limitation commonly referred to as the “black-box” problem (91). This lack of transparency presents a major barrier to clinical trust, regulatory approval, and routine implementation in oncological practice. Explainable artificial intelligence (XAI) has emerged as a promising solution to address these limitations by providing model interpretability alongside predictive accuracy. XAI techniques aim to elucidate the relative importance of input features, highlight salient image regions, and identify clinically meaningful variables that drive model outputs (92). Laios et al. (93) demonstrated the clinical utility of XAI by developing ensem- ble AI models capable of predicting outcomes following cytoreductive surgery for OC, while simultaneously revealing pa- tient- and procedure-specific factors contributing to surgical risk. Subsequent work further extended this framework to predict surgical effort requirements using human-centered and clinically interpretable variables. Despite these advances, many radiomics- based tools and imaging biomarker models discussed in this review continue to function as black-box systems, limiting their reliability and interpretability in real-world clinical settings. This limitation is

  Keywords: clinical models image information patient imaging including model based performance systems contemporary report enhance radiologists