Editorial Comment: Artificial Intelligence in Mammography—Our New Reality

Khanani, Sadia

doi:10.2214/ajr.22.27345

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Computer-aided detection has been used historically for breast cancer imaging, but it did not demonstrate high clinical value. 40 Hence, breast cancer imaging has been a prime target for AIbased cancer detection. For example, AI-based models are now routinely a part of breast imaging and are being used clinically in many practices.…”

Section: Artificial Intelligence Applications In Cancer-related Image...mentioning

confidence: 99%

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

Shreve

Khanani

Haddad

2022

American Society of Clinical Oncology Educational Book

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The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning– and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision–assisted image analysis in particular having several U.S. Food and Drug Administration–approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI’s “black box” mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model’s complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person’s health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.

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Section: Artificial Intelligence Applications In Cancer-related Image...mentioning

confidence: 99%

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

Shreve

Khanani

Haddad

2022

American Society of Clinical Oncology Educational Book

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“…Artificial Intelligence (AI) is revolutionizing numerous medical fields, ranging from image analysis and multi‐omics data integration to drug discovery and precision medicine, with some notable applications already explored [ 1 , 2 ]. Among these, clinical trial design emerges as a promising yet relatively unexplored frontier.…”

Section: Introductionmentioning

confidence: 99%

Unlocking the potential of synthetic patients for accelerating clinical trials: Results of the first GIMEMA experience on acute myeloid leukemia patients

Piciocchi,

Cipriani,

Messina

et al. 2024

eJHaem

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Artificial Intelligence has the potential to reshape the landscape of clinical trials through innovative applications, with a notable advancement being the emergence of synthetic patient generation. This process involves simulating cohorts of virtual patients that can either replace or supplement real individuals within trial settings. By leveraging synthetic patients, it becomes possible to eliminate the need for obtaining patient consent and creating control groups that mimic patients in active treatment arms. This method not only streamlines trial processes, reducing time and costs but also fortifies the protection of sensitive participant data. Furthermore, integrating synthetic patients amplifies trial efficiency by expanding the sample size. These straightforward and cost‐effective methods also enable the development of personalized subject‐specific models, enabling predictions of patient responses to interventions. Synthetic data holds great promise for generating real‐world evidence in clinical trials while upholding rigorous confidentiality standards throughout the process. Therefore, this study aims to demonstrate the applicability and performance of these methods in the context of onco‐hematological research, breaking through the theoretical and practical barriers associated with the implementation of artificial intelligence in medical trials.

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Editorial Comment: Artificial Intelligence in Mammography—Our New Reality

Cited by 2 publications

References 3 publications

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

Unlocking the potential of synthetic patients for accelerating clinical trials: Results of the first GIMEMA experience on acute myeloid leukemia patients

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