In recent years, Artificial Intelligence (AI) has been widely used in
medicine, particularly in the analysis of medical imaging, which has been
driven by advances in computer vision and deep learning methods. This is
particularly important in overcoming the challenges posed by diseases such as
Bone Metastases (BM), a common and complex malignancy of the bones. Indeed,
there have been an increasing interest in developing Machine Learning (ML)
techniques into oncologic imaging for BM analysis. In order to provide a
comprehensive overview of the current state-of-the-art and advancements for BM
analysis using artificial intelligence, this review is conducted with the
accordance with PRISMA guidelines. Firstly, this review highlights the clinical
and oncologic perspectives of BM and the used medical imaging modalities, with
discussing their advantages and limitations. Then the review focuses on modern
approaches with considering the main BM analysis tasks, which includes:
classification, detection and segmentation. The results analysis show that ML
technologies can achieve promising performance for BM analysis and have
significant potential to improve clinician efficiency and cope with time and
cost limitations. Furthermore, there are requirements for further research to
validate the clinical performance of ML tools and facilitate their integration
into routine clinical practice.

医学影像分析中广泛应用人工智能技术，特别是在骨转移的分析中，机器学习方法取得了有希望的性能，具有显著的潜力提高临床效率，应进一步研究和验证其临床性能并促进其融入常规临床实践。

骨转移分析中的人工智能：当前进展、机遇与挑战

Artificial Intelligence in Bone Metastasis Analysis: Current  Advancements, Opportunities and Challenges

This research pioneers the use of fine-tuned Large Language Models (LLMs) to
automate Systematic Literature Reviews (SLRs), presenting a significant and
novel contribution in integrating AI to enhance academic research
methodologies. Our study employed the latest fine-tuning methodologies together
with open-sourced LLMs, and demonstrated a practical and efficient approach to
automating the final execution stages of an SLR process that involves knowledge
synthesis. The results maintained high fidelity in factual accuracy in LLM
responses, and were validated through the replication of an existing
PRISMA-conforming SLR. Our research proposed solutions for mitigating LLM
hallucination and proposed mechanisms for tracking LLM responses to their
sources of information, thus demonstrating how this approach can meet the
rigorous demands of scholarly research. The findings ultimately confirmed the
potential of fine-tuned LLMs in streamlining various labor-intensive processes
of conducting literature reviews. Given the potential of this approach and its
applicability across all research domains, this foundational study also
advocated for updating PRISMA reporting guidelines to incorporate AI-driven
processes, ensuring methodological transparency and reliability in future SLRs.
This study broadens the appeal of AI-enhanced tools across various academic and
research fields, setting a new standard for conducting comprehensive and
accurate literature reviews with more efficiency in the face of ever-increasing
volumes of academic studies.

本研究探索使用精细调整的大型语言模型（LLMs）自动化系统性文献综述（SLRs），提出了将人工智能与学术研究方法相结合的重要和创新贡献。通过采用最新的精细调整方法和开源的 LLMs，我们展示了一种实用高效的自动化 SLR 过程的方法，包括知识综合的最终执行阶段。研究结果在 LLM 响应的事实准确性方面保持了很高的保真度，并通过对现有符合 PRISMA 的 SLR 的复制进行了验证。研究提出了减轻 LLM 虚幻感的解决方案，并提出了追踪 LLM 响应与信息来源的机制，从而证明了这种方法如何满足学术研究的严格需求。最终的发现证实了精细调整的 LLMs 在简化各种劳动密集型的文献综述过程方面的潜力。鉴于这种方法的潜力及其在所有研究领域的适用性，这项基础研究还呼吁更新 PRISMA 报告指南以整合 AI 驱动的过程，确保未来 SLRs 的方法透明性和可靠性。该研究拓宽了 AI 增强工具在各学术和研究领域的应用，为在日益增长的学术研究数量面前以更高效的方式进行全面准确的文献综述设立了新的标准。