Perspectives in Oncology Management and Research

Artificial Intelligence in Oncology Clinical Practice: From Initial Patient Assessment to Outcome Prediction

Abstract Artificial intelligence (AI) is increasingly embedded across oncology workflows, with applications spanning initial assessment, diagnostic work-up, treatment selection, longitudinal monitoring, and survivorship care. A clinically oriented synthesis of these use cases is needed to guide real-world adoption and highlight implementation challenges. A narrative review of recent clinical, translational, and health-services literature was conducted, focusing on AI tools deployed in routine or near-term oncology practice. The manuscript organizes evidence along the cancer care continuum, from first presentation to outcome prediction, and integrates ethical, legal, and regulatory perspectives. AI systems now support ambient documentation and natural language processing (NLP) for initial consultations, advanced imaging and digital pathology for diagnosis, and multimodal decision support for systemic therapy, radiation, and surgery. Additional applications include toxicity and adverse-event prediction, real-time symptom and liquid biopsy monitoring, risk-adapted follow-up, and survivorship risk stratification, although prospective validation and interoperability remain uneven. AI has moved from proof-of-concept to a practical adjunct to oncology decision-making, with demonstrable potential to enhance precision, efficiency, and patient experience across the cancer continuum. Realizing this promise will require validated, explainable, and equitable systems, robust data governance, and deliberate design of human–AI collaboration within everyday oncology practice. AI would not be a replacement for human expertise, but a pivotal tool to aid cancer patient care.

Federated Learning in Precision Oncology: Bridging Multi-Institutional Artificial Intelligence and Clinical Implementation

Precision oncology, an evolving branch of medical science, is increasingly dependent on the application of AI techniques to aid accurate diagnosis, molecular profiling, proper therapy selection, and outcome prediction in cancer patients. However, several problems such as fragmented health care data, institutional barriers, and privacy restrictions related to the use of patient data continue to impede the development of clinically applicable and generalizable AI models. Federated learning (FL) is a newly developed paradigm that enables collaborative creation of AI models without compromising data privacy. In this commentary, we describe how FL is evolving into an essential component across all stages of the precision oncology process, including areas such as radiomics, digital pathology, genomics, multi-omics analysis, molecular tumor board decision-making, patient matching in clinical trials, and development of multimodal AI algorithms. We also address the issue of transitioning from a model with strong technical performance to one that demonstrates good clinical performance by considering aspects of prospective validation, integration into workflows, model interpretability, cybersecurity, governance, and regulation. FL should be thought of as crucial infrastructure rather than just an efficient machine-learning technique for privacy-sensitive environments.

Autonomous AI in prostate cancer: the road ahead towards clinical implementation.

Artificial Intelligence (AI) for detecting clinically significant prostate cancer (csPCa) on MRI has achieved diagnostic performance comparable to that of radiologists. By autonomously interpreting examinations, AI could improve workflow efficiency and help address increasing imaging demands and radiologist shortages. Despite this promise, autonomous AI has not been implemented in clinical practice. This narrative review explores remaining technical and societal barriers to deploying autonomous csPCa detection. We focus on three key domains: limitations in the current evidence base, safety issues and mitigation strategies, and the perspectives of patients and radiologists. Our findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment. Moreover, patients and radiologists show limited acceptance of autonomous AI, although this may improve with greater transparency, targeted education, and clearer guidelines on medico-legal responsibilities. Addressing these challenges is essential to the responsible deployment of autonomous AI and to realizing its efficiency gains in clinical practice.

Artificial Intelligence in Liver Transplantation: A Systematic Review.

INTRODUCTION Artificial intelligence (AI) is increasingly recognized as a transformative paradigm within transplantation medicine, offering advanced computational approaches capable of integrating heterogeneous clinical, biological, imaging, and molecular datasets to improve predictive accuracy and decision-making. Liver transplantation represents a uniquely complex clinical domain characterized by high-dimensional data, nonlinear interactions among risk factors, and critical time-dependent decision processes, thereby providing an ideal context for AI-enabled analytics. METHODS The objective of this systematic review was to critically synthesize current evidence regarding AI applications in liver transplantation, with emphasis on data modalities, algorithmic methodologies, targeted clinical outcomes, validation strategies, and reported performance metrics. A comprehensive search of MEDLINE, Scopus, and the Cochrane Library identified 1045 records following duplicate removal and automated filtering. RESULTS After screening and eligibility assessment, 65 studies met the inclusion criteria. Laboratory data represented the most frequently utilized input (n = 35), followed by clinical (n = 28), demographic (n = 19), imaging (n = 13), and genetic or molecular data (n = 5), with several studies employing multimodal integration. Deep-learning architectures and neural network-based approaches predominated, with additional contributions from ensemble learning methods and conventional machine-learning algorithms. Across multiple clinical domains-including diagnostic classification, prognostic modeling, graft survival prediction, and treatment optimization-AI systems demonstrated high predictive performance, frequently surpassing traditional risk stratification tools such as model for end-stage liver disease and Survival Outcomes Following Liver Transplantation scores. Imaging-based models achieved particularly strong segmentation accuracy, whereas genomic and molecular approaches demonstrated excellent discriminative capability in oncologic and graft-related outcomes. CONCLUSIONS Despite these promising findings, significant methodological limitations persist, including data heterogeneity, insufficient external validation, risk of bias, and challenges related to interpretability, fairness, and ethical deployment. Overall, AI represents a highly promising adjunct to clinical decision-making in liver transplantation; however, robust prospective validation, standardized reporting frameworks, and clinically interpretable implementations remain necessary prior to widespread adoption.

Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment

Artificial intelligence (AI) is rapidly expanding across the radiation oncology workflow, with applications spanning imaging, contouring, treatment planning, quality assurance, outcome prediction, workflow automation, and clinical decision support. Although technical progress has accelerated substantially, successful clinical translation remains inconsistent. Many of the challenges limiting implementation are not unique to radiation oncology and have previously emerged across healthcare and other high-stakes industries. In this narrative review, we examine radiation oncology AI through the broader lens of cross-industry AI development and deployment. We first summarize the current landscape of AI applications in radiation oncology and then analyze representative examples of successful and unsuccessful AI implementation from healthcare and other sectors. These experiences reveal recurring themes that strongly influence clinical AI success, including data representativeness, robust validation, workflow-centered design, human-AI collaboration, uncertainty management, bias mitigation, operational boundaries, and continuous performance monitoring. We discuss how these lessons apply directly to radiation oncology, where AI systems must function within complex clinical workflows involving imaging, planning, adaptive treatment, quality assurance, and longitudinal patient management. Emerging agentic and multimodal AI systems further amplify both opportunities and risks associated with deployment. Ultimately, the future impact of AI in radiation oncology will likely depend less on isolated algorithmic performance than on the development of trustworthy clinical AI ecosystems. Successful implementation will require rigorous validation, seamless workflow integration, human oversight, regulatory governance, and continuous adaptation. Lessons from healthcare and other industries suggest that the greatest and most durable clinical value may arise from AI systems that augment human expertise, cognitive workflows, and multidisciplinary decision-making rather than replace clinical decision-makers.

Artificial intelligence in breast cancer imaging: a systematic review of diagnostic performance, predictive modeling, and clinical translation.