Cyberknife Radiosurgery for Synovial Sarcoma Metastasizing to the Spine: Illustrative Case Reports

ChatGPT in medicine: A cross-disciplinary systematic review of ChatGPT's (artificial intelligence) role in research, clinical practice, education, and patient interaction

BACKGROUND

ChatGPT, a powerful AI language model, has gained increasing prominence in medicine, offering potential applications in healthcare, clinical decision support, patient communication, and medical research. This systematic review aims to comprehensively assess the applications of ChatGPT in healthcare education, research, writing, patient communication, and practice while also delineating potential limitations and areas for improvement.

METHOD

Our comprehensive database search retrieved relevant papers from PubMed, Medline and Scopus. After the screening process, 83 studies met the inclusion criteria. This review includes original studies comprising case reports, analytical studies, and editorials with original findings.

RESULT

ChatGPT is useful for scientific research and academic writing, and assists with grammar, clarity, and coherence. This helps non-English speakers and improves accessibility by breaking down linguistic barriers. However, its limitations include probable inaccuracy and ethical issues, such as bias and plagiarism. ChatGPT streamlines workflows and offers diagnostic and educational potential in healthcare but exhibits biases and lacks emotional sensitivity. It is useful in inpatient communication, but requires up-to-date data and faces concerns about the accuracy of information and hallucinatory responses.

CONCLUSION

Given the potential for ChatGPT to transform healthcare education, research, and practice, it is essential to approach its adoption in these areas with caution due to its inherent limitations.

Single isocenter dynamic conformal arcs-based radiosurgery for brain metastases: Dosimetric comparison with Cyberknife and clinical investigation

Purpose

To compare the dosimetric quality of automatic multiple brain metastases planning (MBM) with that of Cyberknife (CK) based on the clinical tumor condition, such as the tumor number, size, and location.

Methods

76 treatment plans for 46 patients treated with CK were recalculated with the MBM treatment planning system. Conformity index (CI), homogeneity index (HI), gradient index (GI), lesion underdosage volume factor (LUF), healthy tissue overdose volume factor (HTOF), geometric conformity index (g) and mean dose to normal organs were compared between CK and MBM for tumor number, size, shape and distance from the brainstem or chiasm.

Results

The results showed that the mean brain dose was significantly smaller in MBM than CK. CI did not differ between MBM and CK; however, HI was significantly more ideal in CK (p = 0.000), and GI was significantly smaller in MBM (P = 0.000). LUF was larger in CK (p = 0.000) and HTOF and g was larger in MBM (p = 0.003, and 0.012). For single metastases, CK had significantly better HTOF (p = 0.000) and g (p = 0.002), but there were no differences for multiple tumors. Brain dose in MBM was significantly lower and CI was higher for tumors < 30 mm (p = 0.000 and 0.000), whereas HTOF and g for tumors < 10 mm were significantly smaller in CK (p = 0.041 and p = 0.016). Among oval tumors, brain dose, GI and LUF were smaller in MBM, but HTOF and g were smaller in CK. There were no particular trends for tumors close to the brainstem, but HTOF tended to be smaller in CK (0.03 vs. 0.29, p = 0.068) for tumors inside the brainstem.

Conclusions

MBM can reduce the brain dose while achieving a dose distribution quality equivalent to that with CK.

The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework

The objective of this study is to explore the use of ChatGPT (Chat-Generative Pre-Trained Transformer) in neurosurgery and its potential impact on the field. The authors aim to discuss, through a systematic review of current literature, how this rising new artificial intelligence (AI) technology may prove to be a useful tool in the future, weighing its potential benefits and limitations. The authors conducted a comprehensive and systematic literature review of the use of ChatGPT and its applications in healthcare and different neurosurgery topics. Through a systematic review of the literature, with a search strategy using the databases such as PubMed, Google Scholar, and Embase, we analyzed the advantages and limitations of using ChatGPT in neurosurgery and evaluated its potential impact. ChatGPT has demonstrated promising results in various applications, such as natural language processing, language translation, and text summarization. In neurosurgery, ChatGPT can assist in different areas such as surgical planning, image recognition, medical diagnosis, patient care, and scientific production. A total of 128 articles were retrieved from databases, where the final 22 articles were included for thorough analysis. The studies reviewed demonstrate the potential of AI and deep learning (DL), through language models such as ChatGPT, to improve the accuracy and efficiency of neurosurgical procedures, as well as diagnosis, treatment, and patient outcomes across various medical specialties, including neurosurgery. There are, however, limitations to its use, including the need for large datasets and the potential for errors in the output, which most authors concur will need human verification for the final application. Our search demonstrated the potential that ChatGPT holds for the present and future, in accordance with the studies' authors' findings herein analyzed and expert opinions. Further research and development are required to fully understand its capabilities and limitations. AI technology can serve as a useful tool to augment human intelligence; however, it is essential to use it in a responsible and ethical manner.