Recent literature from 2025 to 2026 has firmly established AI as a core driver in the methodological evolution of precision oncology for HCC

The gap

Evidence profile

Sourced from the future work of the source papers, classified as general, spanning 6 journals. Those papers have been cited 2 times in total.

Supporting evidence — 8 representative gaps

• Multimodal data fusion: integrating PET/MRI and liquid biopsy for a holistic view of cancer biology (2026) · Frontiers in Oncology · doi

  Conflict of interest The deep multimodal data fusion of PET/MRI and liquid biopsy is driving the evolution of cancer diagnosis and treatment toward a multimodal approach. This strategy synergizes macroscopic imag- ing information with microscopic molecular data, demonstrating clear value in early tumor detection, heterogeneity analysis, dy- namic treatment monitoring, and precise prognostic stratification. However, its clinical translation still faces core challenges, including a lack of standardization, algorithmic bottlenecks, and insufficient high-level evidence. Moving forward, leveraging artificial intelli- gence and multi-omics technologies to build standardized data analysis platforms and validate clinical utility through prospective trials will be essential. The advancement of this integrated paradigm will provide critical technical support for the transition from population-based treatments to individualized precision medicine, ultimately enhancing the systematic and effective management of cancer. Future directions include: developing AI-driven, multimodal data fusion platforms to achieve end-to-end optimization from raw data to clinical decision-making; exploring the integration of multi-dimensional liquid biopsies beyond blood (such as cerebro- spinal fluid and urine) with site-specific imaging for specialized types like central nervous system tumors; building personalized dynamic monitoring networks based on regular liquid biopsies and key time-point PET/MRI scans to enable predictive healthcare; and ultimately forming a closed-loop, integrated diagnostic and therapeutic system. For instance, by leveraging The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

  generalfuture work
  Keywords: multimodal liquid clinical interest fusion cancer treatment monitoring leveraging multi platforms integrated based ultimately biopsies
• Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer (2026) · Oncology Reviews · 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

  generalfuture work
  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) · Greenfort International Journal of Applied Medical Science · 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.

  generalfuture work
  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) · Frontiers in Oncology · 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.

  generalfuture work
  Keywords: within beyond complex must post causality applications recent literature rmly established core driver methodological evolution
• Novel Biomarkers for Early Detection and Risk Stratification in Chronic Kidney Disease (2026) · KIDNEYS · doi

  To change the current status of CKD in the world, equal access to proven biomarkers and predictive instruments will be needed, especially in the low- and middle- the disease burden income nations where is large. Major multinational disproportionately partnerships are necessary to guarantee the validation of biomarkers in different populations and health care systems. Even smaller projects such as the Global Kidney Health Atlas show that there is a necessity to organize research methods, data repositories, and policy-level engagement in improving kidney care infrastructures at a global scale [45]. The combination of these interventions will contribute to transforming CKD management into an inclusive, predictive, and preventative, and globally precision medicine system. One of the notable future directions of precision nephrology is incorporation of multi-omics information and transcriptomic, like metabolomic layers. High-frequency and pathway- based techniques add strength to prognostic gene signatures, which can be applied in the personalized risk prediction and target therapeutic platforms in CKD populations [46]. Optimal computation models also result in the practical examination of heterogeneous omics information, that is, the recognition of molecular pathways involving the development of diseases [47]. AI and machine learning are highly likely to become even more central to the process of biomarker discovery and clinical risk stratification of chronic kidney disease. Big-data analytics can be used to work with the analysis of complex, high-dimensional clinical data, enhancing the modeling of disease and facilitating data-driven decision-making in nephrology care [48]. AI systems that can predict risk continuously and whose clinical applications can be used to identify patients with a risk proteomic genomic,

  generalfuture work
  Keywords: risk disease care kidney clinical biomarkers predictive populations health systems even global precision nephrology omics
• Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology (2026) · Frontiers in Oncology · doi

  Future developments in AI-assisted prostate cancer diagnostics are likely to focus on improving clinical applicability, generalizabil- ity, and seamless integration into diagnostic and therapeutic workflows (63). Large multicenter collaborations and federated learning frameworks may facilitate the development of more robust and representative models while preserving patient privacy and data security (64). At the same time, advances in explainable AI are expected to improve model transparency by enabling clinicians to better understand the histological features driving algorithm predictions, thereby increasing trust, supporting regulatory accep- tance, and facilitating clinical implementation (65, 66). Beyond conventional histopathology, AI is increasingly being integrated with molecular, genomic, and spatially resolved technol- ogies. Combining histomorphological assessment with spatial transcriptomics, proteomics, and other omics platforms may pro- vide deeper insights into tumor heterogeneity, biological behavior, therapeutic response, and mechanisms of disease progression, thereby supporting more precise risk stratification and personalized treatment selection (67–70). Similarly, AI-assisted analysis of im- munohistochemical biomarkers may enable more objective and reproducible quantification of biomarker expression, improving diagnostic accuracy, prognostic assessment, and patient selection for targeted therapeutic strategies in selected clinical settings (71). Future advances are also likely to support the development of integrated multimodal models that combine histopathological, molecular, radiological, and clinical data to generate comprehensive patient-specific risk profiles. Such approaches may further refine prognostic prediction and facilitate precision oncology by identify- ing patients most likely to benefit from active surveillance, treat- ment intensification, or targeted therapies. Ultimately, the future of AI in prostate cancer diagnosis is likely to depend on effective human–AI collaboration rather than full automa- tion. In this paradigm, AI serves as a decision-support tool that enhances diagnostic consistency, efficiency, quantitative assessment, and risk stratification, while final clinical and pathological decisions remain under expert supervision (72). Such collaborative approaches are likely to maximize the benefits of AI while preserving the clinical judgment essential for individualized patient care.

  generalfuture work
  Keywords: clinical likely patient cation future diagnostic therapeutic assessment risk assisted prostate cancer improving facilitate development
• AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies—a scoping review (2026) · Frontiers in Medicine · doi

  MASLD has emerged as a pressing global health concern, marked by its deceptive progression and association with metabolic disorders. The complexity of MASLD, from benign steatosis to hepatocellular carcinoma, demands a paradigm shift from conventional diagnostic and prognostic workflows to precision-guided, data-driven solutions. This comprehensive review has underscored the growing influence of AI across the MASLD care continuum spanning clinical, imaging, and multi- omics landscapes. The integration of AI into imaging has shown promise in enhancing early detection, automating grading, and improving spatial resolution of liver abnormalities. Meanwhile, machine learning applied to clinical and omics data has enabled stratified risk modeling and biomarker discovery with unprecedented depth. However, despite these advancements, the field remains fragmented siloed by modality-specific innovations, region- limited datasets, and a lack of deployment-ready frameworks. The need for interoperability, clinical validation, and fairness- aware models remains critical. Moving forward, the roadmap for AI in MASLD care must prioritize translational readiness and equitable access. Future research must focus on developing multi-modal architectures capable of jointly analyzing clinical parameters, radiological signals, and molecular phenotypes. AI systems of the future could potentially utilize cascade learning fused with information from multiple modalities. AI systems in the future might use the combination of cascade learning with multi-modal imaging technology, the clinical approach, and multi-omics methodology to better classify the diseases. The information fusion technique established by Delfan et al. serves as a basic principle that can aid in the creation of similar multi-modal AI systems for MASLD. Approved drugs for MASH have now taken over a more important role for AI in the clinical setting that goes beyond diagnosis and classification. Still, there is a growing demand for AI models to predict eligibility for treatment—especially for patients with significant fibrosis (stages F2–F3)—as well as outcomes of therapy and non-invasive follow-up (63, 73). There is also an urgent requirement for longitudinal datasets that can power progression-aware models and anticipate transitions across disease stages. Further, FL and privacy- preserving mechanisms need to be mainstreamed, enabling data systems without compromising sharing across healthcare confidentiality. Moreover, explainability will be a non-negotiable component inherently in gaining clinical trust. Developing interpretable models or robust post-hoc explainers that resonate with clinician logic will be crucial. Additionally, AI systems must incorporate mechanisms for adaptive learning, wherein refines post-deployment predictions over time. dynamically feedback clinical Future research should also consider the issue of the cost- effectiveness and budget impact of MASLD care with the aid of AI, in addition to the accuracy of diagnosis. The evidence base illustrating the health economics of AI-assisted MASLD care will be crucial to support its broad implementation, especially in countries with limited healthcare resources (15, 63, 71). combine systems must Future MASLD-AI technical innovation with practical clinical usability. The success of AI in MASLD will depend on creating human-in-the-loop systems that fit into clinical workflows, meet regulatory standards, and address ethical concerns. By fostering collaborations across clinicians, computer scientists, bioinformaticians, and regulatory bodies, the field can chart a course toward AI systems that are not only smart but safe, scalable, and socially responsible. The next chapter in MASLD research will not be written by AI alone but by its synergistic partnership with the clinical world.

  generalfuture work
  Keywords: clinical masld systems multi future across care learning models must imaging omics modal health progression
• Promises and challenges of AI-enabled methods for myocardial characterisation in cardiovascular magnetic resonance (2026) · Frontiers in Cardiovascular Medicine · cited 2× · doi

  AI offers substantial advantages for tissue characterisation in CMR, with the potential to enhance diagnostic accuracy, improve risk modelling, and deepen disease understanding (Table 2). Direct clinical benefits include real-time quality control during image acquisition (55) and real-time detection of pathology. AI- based reconstruction using undersampling strategies can markedly accelerate acquisition and may be particularly impactful for low- field CMR resource requirements, and improved safety profile offer a more scalable route to expanding access to cardiac MRI (56). systems, whose lower cost, reduced deep End-to-end representation. Future developments may include AI-driven co-registration of multiple CMR modalities—such as cines, LGE, DTI, and parametric maps—into a unified and more coherent three- dimensional learning approaches for probabilistic risk prediction from CMR images are also likely to expand, with explainable AI supporting interpretability and clinician trust. Finally, just as clinicians integrate clinical variables, ECG, and imaging to guide care, multimodal AI is expected to enable integration of these data at greater dimensionality and scale, supporting more accurate risk stratification and personalised therapy than previously possible. Clinicians alongside scientific and technical experts will be central fairness, this generalisability, and robust performance for clinical care. to overseeing evolution, ensuring

  generalfuture work
  Keywords: risk clinical include real time acquisition supporting clinicians care offers substantial advantages tissue characterisation potential

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