Diagnostic Reading #23: Five “Must Read” Articles on HIT and Radiology
Reading Time: 3 minutes read
Precision medicine and smartphones in healthcare are in the headlines this week.
This week’s articles in Diagnostic Reading include: a possible breakthrough for precision medicine; how smartphones are used as medical devices; deep learning more efficient in predicting lung cancer outcomes; strategies to improve patient experiences; and combined DBT, DM improve breast cancer detection.
Two burgeoning technologies have precision medicine on the cusp of a big breakthrough – AI in Healthcare
The simultaneous advances of deep learning and radiomics may soon yield a single unified framework for clinical decision support that has the potential to “completely revolutionize the field of precision medicine,” according to scholars at Johns Hopkins, whose analysis of the technologies is in the journal Expert Review of Precision Medicine and Drug Development. Precision medicine, aka “personalized” medicine, is an approach to disease treatment and prevention that incorporates data on genetics, environment and lifestyle at the level of the individual patient.
8 ways smartphones are being used as medical devices – Health Data Management
Smartphones are quickly gaining the capabilities to make patients’ homes an extension of physicians’ offices, facilitating access to timely medical care. Medical device manufacturers and software engineers continue to take advantage of smartphones’ capabilities, adapting them to serve as diagnostic tools, while clinicians are beginning to use them in their practices as well. This article spotlights some of the current capabilities used to expand the clinical usefulness of smartphones.
Deep learning predicts lung cancer mortality better than clinical model – Health Imaging
Deep learning models predicted survival outcomes better than a standard clinical model by analyzing tumor scans taken from patients with lung cancer, according to a study published in Clinical Cancer Research. Compared to the clinical model, the deep learning methods were more efficient in predicting metastasis, progression and local regional recurrence.
How to improve patient interactions – Diagnostic Imaging
In today’s healthcare environment, there’s an increasing expectation that a radiologist’s contribution to patients’ clinical experiences will go beyond capturing and reading an image. With increasing workflow needs and reporting requirements, creating a relaxed atmosphere for patients can be difficult. This article provides several strategies to help give patients a better experience.
DBT detects additional lesions during breast cancer staging – Health Imaging
Combining digital breast tomosynthesis (DBT) with digital mammography (DM) can spot additional lesions in patients with breast cancer, reported authors of a recent study published in Radiology. In a related editorial, a physician wrote that more research is needed to determine how DBT’s detection of additional cancers impacts long-term health outcomes, but maintained the results add to the growing literature on DBT.
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