James C. L. Chow’s research while affiliated with University Health Network and other places

Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the current state of QC, with a particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, as well as the evolution of QC from theoretical foundations to practical advancements. The paper covers significant milestones where QC has intersected with medical research, including breakthroughs in drug discovery, molecular modeling, genomics, and medical diagnostics. Additionally, key quantum techniques such as quantum algorithms, quantum machine learning (QML), and quantum-enhanced imaging are explained, highlighting their relevance in healthcare. The paper also addresses challenges in the field, including hardware limitations, scalability, and integration within clinical environments. Looking forward, the paper discusses the potential for quantum–classical hybrid systems and emerging innovations in quantum hardware, suggesting how these advancements may accelerate the adoption of QC in medical research and clinical practice. By synthesizing reliable knowledge and presenting it through a comprehensive lens, this paper serves as a valuable reference for researchers interested in the transformative potential of QC in medicine.

Following the successful publication of the first edition of our Special Issue entitled “Application of Nanomaterials in Biomedical Imaging and Cancer Therapy” [...]

The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.

UNSTRUCTURED The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.

This study investigates how scattering foil materials and sampling holder placement affect electron energy distribution in electron beams from a modified medical linear accelerator for FLASH radiotherapy. We analyze electron energy spectra at various positions—ionization chamber, mirror, and jaw—to evaluate the impact of Cu, Pb-Cu, Pb, and Ta foils. Our findings show that close proximity to the source intensifies the dependence of electron energy distribution on foil material, enabling precise beam control through material selection. Monte Carlo simulations are effective for designing foils to achieve desired energy distributions. Moving the sampling holder farther from the source reduces foil material influence, promoting more uniform energy spreads, particularly in the 0.5–10 MeV range for 12 MeV electron beams. These insights emphasize the critical role of tailored material selection and sampling holder positioning in optimizing electron energy distribution and fluence intensity for FLASH radiotherapy research, benefiting both experimental design and clinical applications.

The advent of FLASH radiotherapy (FLASH-RT) has brought forth a paradigm shift in cancer treatment, showcasing remarkable normal cell sparing effects with ultra-high dose rates (>40 Gy/s). This review delves into the multifaceted mechanisms underpinning the efficacy of FLASH effect, examining both physicochemical and biological hypotheses in cell biophysics. The physicochemical process encompasses oxygen depletion, reactive oxygen species, and free radical recombination. In parallel, the biological process explores the FLASH effect on the immune system and on blood vessels in treatment sites such as the brain, lung, gastrointestinal tract, skin, and subcutaneous tissue. This review investigated the selective targeting of cancer cells and the modulation of the tumor microenvironment through FLASH-RT. Examining these mechanisms, we explore the implications and challenges of integrating FLASH-RT into cancer treatment. The potential to spare normal cells, boost the immune response, and modify the tumor vasculature offers new therapeutic strategies. Despite progress in understanding FLASH-RT, this review highlights knowledge gaps, emphasizing the need for further research to optimize its clinical applications. The synthesis of physicochemical and biological insights serves as a comprehensive resource for cell biology, molecular biology, and biophysics researchers and clinicians navigating the evolution of FLASH-RT in cancer therapy.

BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis. METHODS: This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit. RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful. CONCLUSIONS: The model provided means for storing and updating patients’ data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.