The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods
Open accessAbstract
OBJECTIVE: Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the most talked about and controversial topics in radiology and medicine. Over the past few years, the numbers of ML- or AI-focused studies in the literature have increased almost exponentially, and ML has become a hot topic at academic and industry conferences. However, despite the increased awareness of ML as a tool, many medical professionals have a poor understanding of how ML works and how to critically appraise studies and tools that are presented to us. Thus, we present a brief overview of ML, explain the metrics used in ML and how to interpret them, and explain some of the technical jargon associated with the field so that readers with a medical background and basic knowledge of statistics can feel more comfortable when examining ML applications. CONCLUSION: Attention to sample size, overfitting, underfitting, cross validation, as well as a broad knowledge of the metrics of machine learning, can help those with little or no technical knowledge begin to assess machine learning studies. However, transparency in methods and sharing of algorithms is vital to allow clinicians to assess these tools themselves.
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Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers
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Schachner, T., Keller, R., & v Wangenheim, F. (2020) · Journal of Medical Internet Research · doi:10.2196/20701 · Cited by 267
Ellahham, S., Ellahham, N., & Simsekler, M. C. E. (2020) · American Journal of Medical Quality · doi:10.1177/1062860619878515 · Cited by 177
A Scoping Review of Machine Learning in Psychotherapy Research
Aafjes-van Doorn, K., Kamsteeg, C., Bate, J., & Aafjes, M. (2021) · Psychotherapy Research · doi:10.1080/10503307.2020.1808729 · Cited by 179
Metadata
- Authors
- Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., Lee, M. J., & Asadi, H.
- Venue
- American Journal of Roentgenology
- Publisher
- American Roentgen Ray Society
- Published
- 2019-01
- Volume
- 212
- Issue
- 1
- Pages
- 38-43
- ISSN
- 0002-9580 (ISSN-L)
- 0361-803X
- 1546-3141
- OpenAlex
- W2897120700
- Cited by
- 406
- References
- 22
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- green
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- Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., Lee, M. J., & Asadi, H. (2019). Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods. American Journal of Roentgenology. https://doi.org/10.2214/ajr.18.20224
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