| Title |
Demystification of AI-driven medical image interpretation: past, present and future
|
|---|---|
| Published in |
European Radiology, August 2018
|
| DOI | 10.1007/s00330-018-5674-x |
| Pubmed ID | |
| Authors |
Peter Savadjiev, Jaron Chong, Anthony Dohan, Maria Vakalopoulou, Caroline Reinhold, Nikos Paragios, Benoit Gallix |
| Abstract |
The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it. • Artificial intelligence (AI) research in medical imaging has a long history • The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods. • A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view. |
X Demographics
The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Austria | 3 | 15% |
| Spain | 3 | 15% |
| France | 2 | 10% |
| Netherlands | 2 | 10% |
| Hungary | 2 | 10% |
| United Kingdom | 2 | 10% |
| Italy | 1 | 5% |
| United States | 1 | 5% |
| Canada | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 3 | 15% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 35% |
| Scientists | 6 | 30% |
| Practitioners (doctors, other healthcare professionals) | 4 | 20% |
| Science communicators (journalists, bloggers, editors) | 2 | 10% |
| Unknown | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 263 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 263 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 34 | 13% |
| Researcher | 30 | 11% |
| Student > Master | 30 | 11% |
| Student > Bachelor | 24 | 9% |
| Other | 15 | 6% |
| Other | 44 | 17% |
| Unknown | 86 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 64 | 24% |
| Computer Science | 30 | 11% |
| Engineering | 19 | 7% |
| Business, Management and Accounting | 9 | 3% |
| Nursing and Health Professions | 7 | 3% |
| Other | 30 | 11% |
| Unknown | 104 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 17. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 November 2023.
All research outputs
#2,128,329
of 25,059,640 outputs
Outputs from European Radiology
#175
of 4,848 outputs
Outputs of similar age
#42,399
of 336,578 outputs
Outputs of similar age from European Radiology
#7
of 71 outputs
Altmetric has tracked 25,059,640 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,848 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 336,578 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.