Curated Papers > Highly Cited Authoritative Literature on "AI Drug Discovery"

Artificial intelligence in radiology.

Literature Information

DOI	10.1038/s41568-018-0016-5
PMID	29777175
Journal	Nature reviews. Cancer
Impact Factor	66.8
JCR Quartile	Q1
Publication Year	2018
Times Cited	1256
Keywords	Artificial Intelligence, Medical Image Analysis, Deep Learning, Radiology, Oncology
Literature Type	Journal Article, Research Support, N.I.H., Extramural, Review
ISSN	1474-175X
Pages	500-510
Issue	18(8)
Authors	Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, Hugo J W L Aerts

TL;DR

This Opinion article highlights the significant advancements AI algorithms, especially deep learning, have made in medical image analysis, particularly in radiology, where they offer superior pattern recognition and quantitative assessments compared to traditional methods. The authors discuss the implications of these technologies for oncology, the challenges of clinical implementation, and potential pathways for further progress in the field.

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Artificial Intelligence · Medical Image Analysis · Deep Learning · Radiology · Oncology

Abstract

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Primary Questions Addressed

1. How can AI improve the accuracy of disease detection in radiology compared to traditional methods?
2. What are the potential ethical implications of using AI for patient diagnosis in radiology?
3. In what ways can AI algorithms be integrated into existing radiology workflows to enhance efficiency?
4. What specific challenges do AI systems face when analyzing imaging data from diverse populations?
5. How might the future of radiology evolve with the continued advancement of AI technologies in medical imaging?

Key Findings

Research Background and Objectives

Artificial intelligence (AI) has made significant advancements in image recognition, particularly in medical imaging and radiology. The purpose of this opinion article is to explore the implications of AI in radiology, especially in oncology, and to discuss the potential benefits and challenges of integrating AI technologies into clinical practice.

Main Methods/Materials/Experimental Design

The article provides an overview of AI methodologies, focusing on traditional machine learning and deep learning techniques. Key components include:

• Traditional AI: Relies on predefined engineered features to extract characteristics from medical images.
• Deep Learning: Utilizes convolutional neural networks (CNNs) to learn features directly from raw data without explicit feature engineering.

The following flowchart illustrates the AI methodology in medical imaging:

Key Results and Findings

1. AI in Radiology: AI methods can provide quantitative assessments of imaging data, improving accuracy and reproducibility compared to traditional qualitative evaluations by radiologists.
2. Applications in Oncology: AI has been effectively utilized for tasks such as:
   • Detection of abnormalities (e.g., lung nodules, breast lesions).
   • Characterization and segmentation of tumors.
   • Monitoring treatment response through change analysis.
3. Performance Comparison: Studies indicate that deep learning models often match or exceed the performance of radiologists in various diagnostic tasks.

Main Conclusions/Significance/Innovation

The integration of AI into radiology is expected to enhance diagnostic accuracy, reduce workloads, and improve patient outcomes. AI technologies, particularly deep learning, can process vast amounts of imaging data efficiently, thereby addressing the increasing demands on radiologists. The article emphasizes that while AI has transformative potential, it is unlikely to replace radiologists; instead, it will augment their capabilities and facilitate more informed clinical decisions.

Research Limitations and Future Directions

1. Data Curation: The success of AI models heavily relies on high-quality, curated datasets. Current challenges include the need for domain expertise in data labeling and the variability in imaging protocols across institutions.
2. Generalizability: Many AI systems are narrow in scope, performing well on specific tasks but lacking versatility across different imaging modalities and conditions.
3. Ethical and Regulatory Challenges: Concerns regarding data privacy, the interpretability of AI decisions, and the need for regulatory frameworks are highlighted.

Future research should focus on developing robust AI systems capable of generalizing across diverse datasets and tasks, enhancing transparency in AI decision-making processes, and ensuring compliance with ethical standards in patient data usage. Additionally, exploring unsupervised learning techniques could alleviate some data curation challenges.

References

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Literatures Citing This Work

1. We need smarter trigger tools for diagnosing sepsis in children in Canada. - J Mark Ansermino;Matthew O Wiens;Niranjan Kissoon - CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne (2018)
2. Artificial intelligence and nuclear medicine. - Margaret Hall - Nuclear medicine communications (2019)
3. The Continuing Evolution of Molecular Functional Imaging in Clinical Oncology: The Road to Precision Medicine and Radiogenomics (Part I). - Tanvi Vaidya;Archi Agrawal;Shivani Mahajan;Meenakshi H Thakur;Abhishek Mahajan - Molecular diagnosis & therapy (2019)
4. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. - Ahmed Hosny;Chintan Parmar;Thibaud P Coroller;Patrick Grossmann;Roman Zeleznik;Avnish Kumar;Johan Bussink;Robert J Gillies;Raymond H Mak;Hugo J W L Aerts - PLoS medicine (2018)
5. Evaluation of musculoskeletal tumors in the new era of artificial intelligence. - Flávia Martins Costa - Radiologia brasileira (2018)
6. Shining Light Into the Black Box of Machine Learning. - William Hsu;Joann G Elmore - Journal of the National Cancer Institute (2019)
7. Artificial intelligence in cancer imaging: Clinical challenges and applications. - Wenya Linda Bi;Ahmed Hosny;Matthew B Schabath;Maryellen L Giger;Nicolai J Birkbak;Alireza Mehrtash;Tavis Allison;Omar Arnaout;Christopher Abbosh;Ian F Dunn;Raymond H Mak;Rulla M Tamimi;Clare M Tempany;Charles Swanton;Udo Hoffmann;Lawrence H Schwartz;Robert J Gillies;Raymond Y Huang;Hugo J W L Aerts - CA: a cancer journal for clinicians (2019)
8. Decision Support Systems in Oncology. - Seán Walsh;Evelyn E C de Jong;Janna E van Timmeren;Abdalla Ibrahim;Inge Compter;Jurgen Peerlings;Sebastian Sanduleanu;Turkey Refaee;Simon Keek;Ruben T H M Larue;Yvonka van Wijk;Aniek J G Even;Arthur Jochems;Mohamed S Barakat;Ralph T H Leijenaar;Philippe Lambin - JCO clinical cancer informatics (2019)
9. Artificial intelligence in medical imaging of the liver. - Li-Qiang Zhou;Jia-Yu Wang;Song-Yuan Yu;Ge-Ge Wu;Qi Wei;You-Bin Deng;Xing-Long Wu;Xin-Wu Cui;Christoph F Dietrich - World journal of gastroenterology (2019)
10. Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading? - Emre Pakdemirli - Acta radiologica open (2019)

... (1246 more literatures)

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