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Review
. 2024 Apr 29;1(1):ubae006.
doi: 10.1093/bjrai/ubae006. eCollection 2024 Jan.

AI and machine learning in medical imaging: key points from development to translation

Ravi K Samala  1 , Karen Drukker  2 , Amita Shukla-Dave  3   4 , Heang-Ping Chan  5 , Berkman Sahiner  1 , Nicholas Petrick  1 , Hayit Greenspan  6 , Usman Mahmood  4 , Ronald M Summers  7 , Georgia Tourassi  8 , Thomas M Deserno  9 , Daniele Regge  10   11 , Janne J Näppi  12 , Hiroyuki Yoshida  12 , Zhimin Huo  13 , Quan Chen  14 , Daniel Vergara  15 , Kenny H Cha  1 , Richard Mazurchuk  16 , Kevin T Grizzard  17 , Henkjan Huisman  18 , Lia Morra  19 , Kenji Suzuki  20 , Samuel G Armato 3rd  2 , Lubomir Hadjiiski  5
Affiliations
Review

AI and machine learning in medical imaging: key points from development to translation

Ravi K Samala et al. BJR Artif Intell. .

Abstract

Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

Keywords: AI/ML considerations; AI/ML lifecycle; artificial intelligence/machine learning; medical imaging; performance evaluation.

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Conflict of interest statement

Ravi K. Samala – nothing to disclose. Karen Drukker – receives royalties from Hologic. Amita Shukla-Dave – nothing to disclose. Heang-Ping Chan – nothing to disclose. Berkman Sahiner – nothing to disclose. Nicholas Petrick – nothing to disclose. Hayit Greenspan - nothing to disclose. Usman Mahmood – nothing to disclose. Ronald M Summers – received royalties for patents or software licenses from iCAD, Philips, ScanMed, Translation Holdings, PingAn and MGB, received research support from PingAn through a Cooperative Research and Development Agreement, not related to this work. Georgia Tourassi – nothing to disclose. Thomas M. Deserno – nothing to disclose. Daniele Regge – nothing to disclose. Janne J. Näppi – has received royalties from Hologic and from MEDIAN Technologies, through the University of Chicago licensing, not related to this work. Hiroyuki Yoshida – has received royalties from licensing fees to Hologic and Medians Technologies through the University of Chicago licensing, not related to this work. Zhimin Huo – nothing to disclose. Quan Chen – has received compensations from Carina Medical LLC, not related to this work, pro-vides consulting services for Reflexion Medical, not related to this work. Daniel Vergara – nothing to disclose. Kenny Cha – nothing to disclose. Richard Mazurchuk – nothing to disclose. Kevin T. Grizzard – nothing to disclose. Henkjan Huisman – has received grant support from Siemens Healthineers and Canon Medical for a scientific research project, not related to this work. Lia Morra – has received funding from HealthTriagesrl, not related to this work. Kenji Suzuki – provides consulting services for Canon Medical, not related to this work. Samuel G. Armato III – has received royalties and licensing fees for computer-aided diagnosis through the University of Chicago, not related to this work.

Figures

Figure 1.
Figure 1.
Journey of AI/ML in medical imaging.
Figure 2.
Figure 2.
Important considerations in the advancement and translation of AI/ML-enabled medical imaging applications.
See this image and copyright information in PMC

References

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