The Big Picture: PACS vendors vie for piece of software pieBy Kathy Kincade, EditorAs the healthcare industry finally catches up to the rest of the PC-based world, there is a growing need to create new programming and applications-development
By Kathy Kincade, Editor
As the healthcare industry finally catches up to the rest of the PC-based world, there is a growing need to create new programming and applications-development environments tailored to the needs of the medical community. Or is there?
With the increasing sophistication of PACS, certain components are becoming commodities. As a result, some vendors have begun searching for new revenue streams. And with the advent of enterprise-wide image distribution, software represents one potentially lucrative niche. This opportunity has drawn the attention of several PACS and modality vendors, which have created software platforms with the hope of attracting new business from both their customers and their competitors.
But is this just another example of trying to reinvent the wheel? A handful of software developers have been adequately serving this market for many years. In addition, the medical imaging community has been quite successful in its cooperative approach to standards development. Look at DICOM and HL7.
And then there is the open architecture of the PC itself, which overcomes the legacy-system problem and offers a number of non-healthcare-specific application-development tools created and refined by computer companies with immense resources and expertise at their disposal. Software is, after all, their business.
But not necessarily software that is specific to the needs of the medical imaging industry. That requires yet another level of expertise from developers who understand how the medical community works and what tools they need to help them do their jobs better.
© 2000 Miller Freeman, Inc., a United News & Media company
MRI-Based AI Radiomics Model Offers 'Robust' Prediction of Perineural Invasion in Prostate Cancer
July 26th 2024A model that combines MRI-based deep learning radiomics and clinical factors demonstrated an 84.8 percent ROC AUC and a 92.6 percent precision-recall AUC for predicting perineural invasion in prostate cancer cases.
Breast MRI Study Examines Common Factors with False Negatives and False Positives
July 24th 2024The absence of ipsilateral breast hypervascularity is three times more likely to be associated with false-negative findings on breast MRI and non-mass enhancement lesions have a 4.5-fold likelihood of being linked to false-positive results, according to new research.
Can Polyenergetic Reconstruction Help Resolve Streak Artifacts in Photon Counting CT?
July 22nd 2024New research looking at photon-counting computed tomography (PCCT) demonstrated significantly reduced variation and tracheal air density attenuation with polyenergetic reconstruction in contrast to monoenergetic reconstruction on chest CT.