Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection

Marinovich, M. Luke; Wylie, Elizabeth; Lotter, William; Lund, Helen; Waddell, Andrew; Madeley, Carolyn; Pereira, Gavin; Houssami, Nehmat

doi:10.1016/j.ebiom.2023.104498

“…AI has undeniably made impressive progress and for many use-cases can reach diagnostic performance comparable to that of human readers. This has especially been shown in the context of breast cancer screening ( 65 – 69 ). However, as discussed above, many factors can influence the technical diagnostic performance of AI tools in clinical practice.…”

Section: Section 6: What Should Purchasers Of Ai Tools Consider When ...mentioning

confidence: 86%

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA

Brady,

Allen,

Chong

et al. 2024

Radiology: Artificial Intelligence

10

0

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Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI https://doi.org/10.1186/s13244-023-01541-3 ), Journal of Medical Imaging and Radiation Oncology (DOI https://doi.org/10.1111/1754-9485.13612 ), Canadian Association of Radiologists Journal (DOI https://doi.org/10.1177/08465371231222229 ), Journal of the American College of Radiology (DOI https://doi.org/10.1016/j.jacr.2023.12.005 ), and Radiology: Artificial Intelligence (DOI https://doi.org/10.1148/ryai.230513 ). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor’s Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

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“…AI has undeniably made impressive progress and for many use‐cases can reach diagnostic performance comparable to that of human readers. This has especially been shown in the context of breast cancer screening 65–69 . However, as discussed above, many factors can influence the technical diagnostic performance of AI tools in clinical practice.…”

Section: What Should Purchasers Of Ai Tools Consider When Contemplati...mentioning

confidence: 94%

“…This has especially been shown in the context of breast cancer screening. [65][66][67][68][69] However, as discussed above, many factors can influence the technical diagnostic performance of AI tools in clinical practice. While it has been suggested that the combination of human reader and AI tool could help increase overall diagnostic accuracy by either the human detecting an error made by the AI or vice versa, recent studies question this premise and highlight the need to further study the psychological phenomena that can bias decision making in a setting of human-AI interaction.…”

Section: Human-ai Interactionmentioning

confidence: 99%

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi‐society statement from the ACR, CAR, ESR, RANZCR & RSNA

Brady,

Allen,

Chong

et al. 2024

J Med Imag Rad Onc

3

0

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SummaryArtificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‐growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‐society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

show abstract

“…This has especially been shown in the context of breast cancer screening. [65][66][67][68][69] However, as discussed above, many factors can influence the technical diagnostic performance of AI tools in clinical practice. While it has been suggested that the combination of human reader and AI tool could help increase overall diagnostic accuracy by either the human detecting an error made by the AI or vice versa, recent studies question this premise and highlight the need to further study the psychological phenomena that can bias decision making in a setting of human-AI interaction.…”

Section: Human-ai Interactionmentioning

confidence: 99%

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA

Brady,

Allen,

Chong

et al. 2024

Can Assoc Radiol J

4

0

View full text Add to dashboard Cite

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

show abstract

Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection

Cited by 25 publications

References 25 publications

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi‐society statement from the ACR, CAR, ESR, RANZCR & RSNA

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA

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