Parallel Session 5 - Striving for Clinical Excellence through Application of Advanced Technology to Allied Health Services

Hypofractionation-delivering fewer, higher-dose radiotherapy sessions-has transformed cancer care by enhancing treatment quality and optimizing resource efficiency. Enabled by precision technologies like stereotactic body radiotherapy (SBRT) and intensity-modulated radiotherapy (IMRT), this approach achieves non-inferior tumor control for breast, prostate, and lung cancers, supported by landmark trials (e.g., CHHiP, FAST-Forward). Hypofractionation exploits radiobiology, particularly for tumors with low alpha/beta ratios (e.g., prostate cancer), where higher per-fraction doses improve therapeutic efficacy. While acute toxicity (e.g., skin reactions) may rise slightly, late toxicity remains comparable to conventional regimens when advanced techniques minimize damage to healthy tissues.


For patients, hypofractionation improves quality of life by shortening treatment duration (e.g., 1–5 weeks vs. 6–8 weeks), reducing logistical burdens, and increasing accessibility for rural or underserved populations. From a healthcare perspective, fewer sessions lower costs (e.g., ~30% savings in breast cancer), increase machine throughput, and expand capacity, particularly in resource-limited settings. Palliative applications, such as single-fraction bone metastasis treatment, further highlight its efficiency without compromising outcomes.


Challenges include patient selection (avoiding tumors near critical organs), technological disparities limiting global access, and insufficient long-term data for ultra-hypofractionated regimens (e.g., 5-session prostate SBRT). Future priorities involve expanding indications (e.g., pancreatic/liver cancers), integrating immunotherapy to leverage synergistic effects, and promoting equitable adoption through cost-effective protocols and training.


In summary, hypofractionation represents a paradigm shift toward value-based oncology, balancing clinical efficacy with patient-centered and resource-efficient care. While its adoption addresses escalating cancer burdens, equitable implementation and ongoing research remain critical to refine applications and address evidence gaps.

The integration of Large Language Models (LLMs) into radiation therapy represents a transformative advancement, addressing the field's inherent complexity through multimodal intelligence. This report systematically explores the capabilities, applications, challenges, and future directions of LLMs in enhancing precision, efficiency, and patient-centered care in radiotherapy.

The integration of artificial intelligence (AI) into Diagnostic Radiology is both promising and uncertain. In Radiology Department of Prince of Wales Hospital (PWH), we piloted a vendor AI solution, focusing on two modules: lung nodule detection in CT thorax and pulmonary embolism (PE) detection in CT pulmonary angiogram (CTPA). This presentation shares our experience, emphasizing AI's potential to enhance diagnostic workflows while addressing real-world challenges.
For lung nodule detection, internal validation compared AI results with radiologists' findings, revealing promising results in nodule detection. This suggests AI's utility in flagging cases requiring early attention. However, workflow efficiency comparisons (radiologist-only vs. radiologist + AI) showed mixed outcomes: while AI reduced initial screening time for some radiologists, its impact varied across individuals, underscoring the need for workflow optimization.
In PE detection, the AI demonstrated reliable performance in detecting acute emboli from the pulmonary trunk to lobar branches. This capability has the potential to expedite critical alerts to clinicians, shortening time-to-diagnosis for life-threatening PE and enabling faster clinical management, which may finally improve patient outcomes.
Radiographers serve as frontline decision-makers, tasked with identifying cases requiring urgent or earlier radiological reporting. By integrating AI into routine workflows, this empowers radiographers to swiftly pinpoint cases requiring prioritized reporting amidst high patient volumes, leveraging AI-generated findings to shortlist cases with significant findings (e.g., suspicious nodules) or acute conditions (e.g., PE) for expedited radiologists' review. This synergy bridges AI's analytical power with actionable clinical care.
While AI adoption at PWH remains a pilot initiative, our findings underscore its dual role as a supplementary diagnostic tool and workflow accelerator. Challenges such as inconsistent efficiency gains persist, but the technology shows promise in enhancing diagnostic confidence and prioritizing time-sensitive cases.

The rapid advancement in Artificial Intelligence (AI) has significantly transformed the field of radiotherapy, offering opportunities to enhance precision, efficiency, and patient outcome. AI-driven technologies, including machine learning (ML) and deep learning (DL) algorithms, have been integrated into various stages of radiotherapy, such as imaging and treatment planning. One major trend in application is the use of AI for automated contouring of organs and tumors, thus reducing the time-intensive and operator-dependent nature of manual processes. 


Such technologies have been implemented at different phases in HA oncology centers. Despite the opportunities offered by AI, several challenges and pitfalls remain. Bias and quality issues in training datasets can hinder the reliability of AI models. The "black-box" nature of many AI algorithms poses barriers to clinical adoption, as healthcare providers demand interpretability and transparency. AI applications may also create hard-to-detect errors in specific clinical scenarios. Healthcare professionals should therefore be well educated in the subject, and critically evaluate the AI algorithms to be adopted. Creating a sustainable and efficient workflow for the entire treatment process should be the ultimate goal, and AI is an invaluable tool in the task. Besides technologies available on the market, local research institutes are active to incorporate AI into healthcare. Close collaboration with researchers can keep our healthcare professionals abreast on technology frontiers, and also to devise suitable AI solutions in solving clinical problems.


In conclusion, AI presents a transformative opportunity in radiotherapy. Collaborative efforts involving clinicians, allied health professionals and researchers are essential to maximize the benefit of AI.