Anxiety is surfacing over the National Institutes of Health proposal regarding the sharing of medical research data. Under the March 2002 proposal, applicants for NIH funding would be required during grant application to include data sharing plans or
Anxiety is surfacing over the National Institutes of Health proposal regarding the sharing of medical research data.
Under the March 2002 proposal, applicants for NIH funding would be required during grant application to include data sharing plans or justify why data won't be shared. NIH would assess the adequacy of an applicant's plan or the rationale for its absence during the scientific review of the application.
The Association of American Medical Colleges expressed its worries in a May 10 letter that president Dr. Jordan J. Cohen sent in response to NIH's request for comments. The AAMC represents the nation's 125 accredited medical schools, nearly 400 major teaching hospitals, more than 105,000 faculty in 98 academic and scientific societies, and 66,000 medical students and 97,000 residents.
While endorsing the initiative to encourage data sharing, Cohen shared concerns expressed earlier by the Federation of American Societies for Experimental Biology that the proposed policy is premature and, in some cases, unworkable. Cohen went on to say AAMC is also troubled that the data sharing proposal does not fully acknowledge the constraints that pending Health Insurance Portability and Accountability Act rules may have on the sharing of data from clinical trials.
AAMC believes that effective policies to promote data sharing will require creative, discipline-specific solutions to these complicated problems.
"We recommend that prior to mandating data sharing proposals, NIH convene advisory panels composed of experts in the various scientific disciplines and charge them with devising standards and normative practices for data sharing within their respective fields of research," Cohen said.
Not all reactions to the NIH proposal have been negative.
"Medicine could be the only domain among the science-based areas that has been essentially 'hiding' data," said Andrew Kusiak, Ph.D., a professor in the Intelligent Systems Laboratory at the University of Iowa. "Yet clinical medicine is almost entirely data driven. We need to make medical data available, so that, for example, a cancer patient can get the best treatment available, or a drug dosage can be verified based on algorithmically derived knowledge so treatments can be individualized."
Tools and techniques have been developed to extract domain-specific knowledge, especially medical knowledge that depends on rapid discoveries that could enhance decision-making capabilities, he said.
"We should be striving to achieve a goal that one day a computer program would make a better decision than a person does. None of us can analyze thousands of data points," Kusiak said.
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.