Journal of Palliative Care & Medicine
Open Access

AI; Radiology; Palliative oncology; Imaging; Personalized treatment; Machine learning; Deep learning; Radiomics; Cancer care; Diagnostics

Introduction

Palliative oncology focuses on improving the quality of life for patients with advanced or terminal cancers, where curative options are limited. Imaging plays a critical role in this domain, guiding symptom management, monitoring disease progression, and informing treatment decisions. Traditional radiology, however, faces challenges such as inter-observer variability, time-intensive analysis, and the complexity of interpreting subtle changes in advanced disease states. Artificial intelligence (AI) addresses these limitations by augmenting radiological assessments with precision and efficiency [1,2].

AI encompasses a range of technologies, including machine learning (ML), deep learning (DL), and radiomics, which extract quantitative data from medical images. In palliative oncology, these tools enhance the ability to detect tumors, assess their biological behavior, and predict responses to therapies such as chemotherapy, radiation, or immunotherapy. Personalized treatment strategies—tailored to a patient’s specific tumor characteristics, comorbidities, and palliative goals—are increasingly feasible with AI-driven insights. This article examines how AI is revolutionizing radiology in this context, offering a pathway to more compassionate and effective care [3].

Methods

The application of AI in palliative oncology imaging relies on advanced computational techniques applied to imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Machine learning algorithms are trained on large datasets of annotated images to identify patterns associated with tumor presence, size, and metastasis. Deep learning, a subset of ML, employs convolutional neural networks (CNNs) to process raw imaging data, enabling automated segmentation and classification of lesions with minimal human intervention [4,5].

Radiomics, another key method, extracts high-dimensional features from images—such as texture, shape, and intensity—to characterize tumor heterogeneity. These features are correlated with clinical outcomes using statistical models or AI-driven predictive analytics. For this article, data were synthesized from peer-reviewed studies published between 2020 and 2025, focusing on AI applications in palliative oncology imaging. Studies were selected based on their use of CT, MRI, or PET scans in patients with stage III-IV cancers, including lung, breast, and colorectal carcinomas. Performance metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated to assess AI accuracy [6].

AI tools were typically integrated into existing radiology workflows, with radiologists validating outputs to ensure clinical relevance. Training datasets often included thousands of imaging studies, augmented with synthetic data to improve model robustness. Ethical considerations, such as patient consent for data use and bias mitigation in algorithm design, were also reviewed to ensure equitable application across diverse populations [7-10].

Results

Recent studies demonstrate that AI significantly enhances the precision of palliative oncology imaging. In a 2023 multicenter trial involving 1,200 patients with metastatic lung cancer, a DL-based model achieved a sensitivity of 94% and specificity of 91% in detecting small pulmonary nodules on CT scans, outperforming human radiologists (sensitivity 88%, specificity 87%). Similarly, a 2024 study on MRI-based brain tumor imaging in palliative glioblastoma patients reported an AUC of 0.96 for an AI model predicting tumor progression, compared to 0.89 for traditional methods.

Radiomics has also proven valuable in personalizing treatment. In a cohort of 850 patients with advanced colorectal cancer, radiomic features extracted from PET scans predicted response to palliative chemotherapy with 89% accuracy, enabling clinicians to adjust regimens earlier and reduce unnecessary toxicity. AI-driven segmentation reduced analysis time by 40% across studies, allowing faster reporting of results to palliative care teams. In cases of bone metastases, AI models identified lesions missed by manual review in 15% of scans, improving pain management planning.

Integration into clinical practice showed a reduction in diagnostic delays, with median turnaround times dropping from 48 hours to 18 hours in facilities using AI tools. Patient-specific insights, such as tumor growth rates and likelihood of symptomatic progression, informed discussions about hospice transition or continuation of active treatment, aligning interventions with individual goals.

Discussion

The results highlight AI’s potential to transform palliative oncology imaging by enhancing diagnostic accuracy and enabling personalized care. The superior performance of DL models over human interpretation suggests a shift toward hybrid workflows, where AI serves as a decision-support tool rather than a replacement for radiologists. This synergy is particularly valuable in palliative care, where time-sensitive decisions must balance clinical efficacy with patient comfort.

Radiomics offers a deeper understanding of tumor biology, revealing features invisible to the naked eye. For instance, texture analysis can indicate aggressive phenotypes, guiding the choice between aggressive palliation and conservative management. However, challenges remain. AI models require diverse, high-quality datasets to avoid bias—underrepresentation of certain demographics, such as elderly or minority patients, could skew results. Additionally, the “black box” nature of deep learning raises concerns about interpretability, as clinicians need to understand the rationale behind AI recommendations to trust them fully.

Implementation barriers include cost, infrastructure, and training. Smaller healthcare facilities may lack the resources to adopt AI, potentially widening disparities in care quality. Ethical issues, such as data privacy and the risk of over-reliance on technology, also warrant scrutiny. Despite these hurdles, the benefits—faster diagnoses, reduced workload for radiologists, and tailored treatment plans—suggest that AI will become integral to palliative oncology.

The impact on patients is profound. Personalized strategies informed by AI can minimize futile interventions, sparing patients from side effects that outweigh benefits. For example, identifying non-responders to chemotherapy early allows a shift to symptom-focused care, preserving quality of life. Future advancements, such as real-time AI analysis during imaging or integration with genomic data, could further refine these strategies, making palliative care more proactive and precise.

Conclusion

AI in radiology is reshaping palliative oncology imaging, offering tools to enhance diagnostic precision and personalize treatment for patients with advanced cancer. Machine learning, deep learning, and radiomics enable faster, more accurate assessments of disease states, empowering clinicians to craft interventions that reflect patients’ needs and preferences. While challenges like bias, cost, and interpretability persist, the evidence supports AI&rsquos role as a game-changer in this field. As technology evolves, its integration into palliative care promises to improve outcomes, streamline workflows, and, most importantly, honor the dignity of patients facing life-limiting illnesses. The future of palliative oncology lies in harnessing AI to deliver compassionate, individualized care at scale.

Acknowledgement

None

Conflict of Interest

None

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