The impact of ChatGPT and LLMs on medical imaging stakeholders: Perspectives and use cases

Probably not a substantial claim: may not pass S3 (load-bearing).

The answer generated by an LLM for a research problem aligns well with previous studies for the same research questions.

“Notably, the answer aligns well with previous studies for the same research questions [29, 26].”

• cites [29] Yang, 2021, A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-pd-1/pd-l1 immunotherapy in advanced stage non-small-cell lung cancer, Am J Tourism ResNot checked yet

  Not checked in the free run.

• cites [26] Zou, 2018Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

Public health authorities could leverage LLMs to scrutinize large volumes of health data, enhancing disease surveillance capabilities.

“On the other hand, public health authorities could also leverage the analytical prowess of these models to scrutinize large volumes of health data. By identifying trends and patterns, LLMs could significantly enhance disease surveillance capabilities, providing valuable insights for proactive disease control and prevention measures.”

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

The integration of LLMs into the healthcare ecosystem is not just enhancing existing capabilities but also redefining the interactions and balance among the stakeholders, heralding a new era of high-performance medicine.

“Thus, the integration of LLMs into the healthcare ecosystem is not just enhancing existing capabilities but also redefining the interactions and balance among the stakeholders, heralding a new era of high-performance medicine [23]-a paradigm defined by technology-enabled, data-driven healthcare services.”

• cites [23] Zhao, 2018, 3d deep learning from ct scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas, Cancer Res sourceNot checked yet

  Not checked in the free run.

May combine two claims.

LLMs such as ChatGPT are emerging as influential players in the healthcare ecosystem, transforming the roles and operations of each stakeholder.

“In this complex web of interactions, LLMs such as ChatGPT are emerging as influential players. With their unparalleled ability to interpret health data, improve communication, and facilitate decision-making processes, these models are transforming the roles and operations of each stakeholder in the healthcare ecosystem [17, 18].”

• cites [17] Adams, 2023, Leveraging gpt-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study, Radiology sourceNot checked yet

  Not checked in the free run.

• cites [18] Varoquaux, 2022, Machine learning for medical imaging: methodological failures and recommendations for the future, NPJ digital medicine sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

LLMs can aid in answering policyholder queries and providing personalized advice, improving the overall customer experience.

“In addition, LLMs can aid in answering policyholder queries and providing personalized advice, improving the overall customer experience and ensuring policyholders receive the most accurate and helpful information.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

CROs could harness LLMs to re-engineer their data processing and analysis workflows in clinical trials, bolstering the speed and accuracy of their research processes.

“By employing LLMs in data handling and analysis, these organizations can bolster the speed and accuracy of their research processes.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

LLMs can optimize user manuals and device operation guidelines into talking agents, enhancing their clarity and comprehensibility for healthcare professionals.

“The applicability of LLMs can also optimize user manuals and device operation guidelines into talking agents, enhancing their clarity and comprehensibility for healthcare professionals.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

LLMs can identify patterns and anomalies that could signify potential fraudulent activities in insurance claims.

“With the ability to process large volumes of data, LLMs can identify patterns and anomalies that could signify potential fraudulent activities, providing an essential tool for insurance companies to prevent such occurrences.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Insurance entities can use LLMs to enhance customer service, facilitate more accurate risk assessment, and improve fraud detection.

“These models can enhance customer service, facilitate more accurate risk assessment, and improve fraud detection.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Existing AI diagnostic tools augmented by LLMs can facilitate interactive patient consultation, thereby improving patient understanding and engagement.

“Besides, existing AI diagnostic tools augmented by LLMs can facilitate interactive patient consultation, thereby improving patient understanding and engagement.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

LLMs can augment the regulatory review process for medical product submissions.

“For regulatory agencies, LLMscan augment the regulatory review process, assisting in the meticulous scrutiny of medical product submissions.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

LLMs can serve as valuable tools for analyzing copious amounts of user feedback and technical documentation, which could provide significant insights, leading to the evolution of existing medical devices.

“LLMs can serve as valuable tools for analyzing copious amounts of user feedback and technical documentation, which could provide significant insights, leading to the evolution of existing devices.”

May combine two claims.

LLMs can bridge the gap by translating complex medical language into comprehensible layman's terms, thereby improving patient understanding and engagement.

“LLMs can bridge this gap by translating complex medical language into comprehensible layman's terms, thereby improving patient understanding and engagement [3].”

• cites [3] Moor, 2023, Foundation models for generalist medical artificial intelligence, Nature sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

LLMs can assist in the design of research plans by providing insights on experimental design, data analysis methods, or suggesting relevant literature.

“In terms of research, LLMs can assist in the design of research plans by providing insights on experimental design, data analysis methods, or even suggesting relevant literature.”

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

LLMs are adept at interpreting and analyzing extensive biomedical datasets, facilitating more precise conclusions and novel discoveries in research.

“These models are adept at interpreting and analyzing extensive biomedical datasets, facilitating more precise conclusions and novel discoveries.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

Regulatory and legal challenges emerge for LLM integration in medical imaging, requiring clear guidelines from regulatory bodies to protect patient safety and ensure data integrity.

“Regulatory and legal challenges also emerge, as the integration of LLMs requires clear, comprehensive guidelines from regulatory bodies to protect patient safety and ensure data integrity.”

Probably not a substantial claim: may not pass S4 (based).

Efficiency in clinical workflows is enhanced as LLMs automate the reading and interpretation of diagnostic imaging reports, reducing manual workload.

“Efficiency in clinical workflows is enhanced as LLMs automate the reading and interpretation of diagnostic imaging reports, reducing manual workload and allowing healthcare professionals to devote more time to patient care.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

By generating patient-friendly explanations of imaging results, LLMs can enhance patient engagement.

“Furthermore, by generating patient-friendly explanations of imaging results, LLMs can enhance patient engagement, making healthcare a more collaborative and holistic experience.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

LLMs could function as personalized instructors, elucidating complex imaging concepts and results, fostering a more intuitive understanding for students.

“They could function as personalized instructors, elucidating complex imaging concepts and results, thereby fostering a more intuitive understanding for students.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

In clinical report structuring, LLMs can convert a narrative radiology report into a structured format such as JSON, which is easier to process and analyze.

“In clinical report structuring, LLMs can convert a narrative radiology report into a structured format such as JSON, which is easier to process and analyze.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

LLMs have the potential to revolutionize medical education by providing personalized, intuitive, and interactive learning experiences.

“Another major advantage of LLMs is their potential to revolutionize medical education. They can provide personalized, intuitive, and interactive learning experiences, adapting to individual learning styles and pacing.”

May combine two claims.

Bias and fairness are critical issues for LLMs in medical imaging because if training data is inherently biased, these biases could be replicated in AI predictions, leading to unfair outcomes and exacerbating existing healthcare disparities.

“Bias and fairness are also critical issues [30, 20]. LLMs learn from the data they are trained on; if this data is inherently biased, these biases could be replicated in AI predictions, leading to unfair outcomes and exacerbating existing healthcare disparities.”

• cites [30] Gabriel, 2020, Artificial intelligence, values, and alignment, Minds Mach sourceNot checked yet

  Not checked in the free run.

• cites [20] Benjamens, 2020, The state of artificial intelligence-based fda-approved medical devices and algorithms: an online database, NPJ digital medicine sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Radiologists and referring physicians can utilize LLMs as advanced decision-support tools to augment diagnostic precision and streamline imaging processes.

“Radiologists and referring physicians can utilize LLMs as advanced decision-support tools. These models' prowess in interpreting vast quantities of radiology reports can potentially augment diagnostic precision and streamline imaging processes.”

Probably not a substantial claim: may not pass S4 (based).

LLMs can improve diagnostic precision by providing more nuanced and comprehensive interpretations of medical imaging data.

“LLMs can improve diagnostic precision by providing more nuanced and comprehensive interpretations of medical imaging data.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

LLMs' capacity to discern research gaps and generate new hypotheses can catalyze medical research and innovation.

“In addition, their capacity to discern research gaps and generate new hypotheses can catalyze medical research and innovation.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

Patients stand to benefit from LLMs serving as advanced communication platforms that distill complex medical information into more understandable terms, encouraging active participation in healthcare.

“On the other hand, patients, as the central figures in the healthcare landscape, stand to benefit from LLMs serving as advanced communication platforms. These models can distill complex medical information into more understandable terms, encouraging patients to participate actively in their healthcare journey.”