Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

The anticancer drugs have evolved significantly, spanning molecular targeted therapeutics (MTTs), immune checkpoint inhibitors (ICIs), chimeric antigen receptor T-cell (CAR-T) therapy, and antibody-drug conjugates (ADCs). Complications associated with these drugs vary widely based on their mechanisms of action. MTTs that target angiogenesis can often lead to complications related to ischemia or endothelial damage across various organs, whereas non-anti-angiogenic MTTs present unique complications derived from their specific pharmacological actions. ICIs are predominantly associated with immunerelated adverse events, such as pneumonitis, colitis, hepatitis, thyroid disorders, hypophysitis, and sarcoid-like reactions. CAR-T therapy causes unique and severe complications including cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome. ADCs tend to cause complications associated with cytotoxic payloads. A comprehensive understanding of these drug-specific toxicities, particularly using medical imaging, is essential for providing optimal patient care. Based on this knowledge, radiologists can play a pivotal role in multidisciplinary teams. Therefore, radiologists must stay up-to-date on the imaging characteristics of these complications and the mechanisms underlying novel anticancer drugs.

Clinical trials for chimeric antigen receptor (CAR) T-cell therapy are in the early stages but are expected to progress alongside new treatment approaches. This suggests that imaging will play an important role in monitoring disease progression, treatment response, and treatment-related side effects. There are, however, challenges that remain unresolved, regarding imaging in CAR T-cell therapy. We herein discuss the role of imaging, focusing on how tumor response evaluation varies according to cancer type and target antigens in CAR T-cell therapy. CAR T-cell therapy often produces rapid and significant responses, and imaging is vital for identifying side effects such as cytokine release syndrome and neurotoxicity. Radiologists should be aware of drug mechanisms, response assessments, and associated toxicities to effectively support these therapies. Additionally, this article highlights the importance of the Lugano criteria, which is essential for standardized assessment of treatment response, particularly in lymphoma therapies, and also explores other factors influencing imaging-based evaluation, including emerging methodologies and their potential to improve the accuracy and consistency of response assessments.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

The anticancer drugs have evolved significantly, spanning molecular targeted therapeutics (MTTs), immune checkpoint inhibitors (ICIs), chimeric antigen receptor T-cell (CAR-T) therapy, and antibody-drug conjugates (ADCs). Complications associated with these drugs vary widely based on their mechanisms of action. MTTs that target angiogenesis can often lead to complications related to ischemia or endothelial damage across various organs, whereas non-anti-angiogenic MTTs present unique complications derived from their specific pharmacological actions. ICIs are predominantly associated with immunerelated adverse events, such as pneumonitis, colitis, hepatitis, thyroid disorders, hypophysitis, and sarcoid-like reactions. CAR-T therapy causes unique and severe complications including cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome. ADCs tend to cause complications associated with cytotoxic payloads. A comprehensive understanding of these drug-specific toxicities, particularly using medical imaging, is essential for providing optimal patient care. Based on this knowledge, radiologists can play a pivotal role in multidisciplinary teams. Therefore, radiologists must stay up-to-date on the imaging characteristics of these complications and the mechanisms underlying novel anticancer drugs.

Clinical trials for chimeric antigen receptor (CAR) T-cell therapy are in the early stages but are expected to progress alongside new treatment approaches. This suggests that imaging will play an important role in monitoring disease progression, treatment response, and treatment-related side effects. There are, however, challenges that remain unresolved, regarding imaging in CAR T-cell therapy. We herein discuss the role of imaging, focusing on how tumor response evaluation varies according to cancer type and target antigens in CAR T-cell therapy. CAR T-cell therapy often produces rapid and significant responses, and imaging is vital for identifying side effects such as cytokine release syndrome and neurotoxicity. Radiologists should be aware of drug mechanisms, response assessments, and associated toxicities to effectively support these therapies. Additionally, this article highlights the importance of the Lugano criteria, which is essential for standardized assessment of treatment response, particularly in lymphoma therapies, and also explores other factors influencing imaging-based evaluation, including emerging methodologies and their potential to improve the accuracy and consistency of response assessments.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.