DOTmed Home MRI Oncology Ultrasound Molecular Imaging X-Ray Cardiology Health IT Business Affairs
News Home Parts & Service Operating Room CT Women's Health Proton Therapy Endoscopy HTMs Mobile Imaging
SEARCH
Current Location:
>
> This Story


Log in or Register to rate this News Story
Forward Printable StoryPrint Comment
advertisement

 

advertisement

 

Artificial Intelligence Homepage

NYU releases biggest ever MR data set in AI Facebook collaboration With fastMRI, acceleration of imaging by factor of four 'already possible'

Subtle Medical closes RSNA with CE mark and FDA clearance of PET AI solution Speeds up scans by factor of four, enhanced image quality

Infervision showcases new AI concepts at RSNA Detecting four different conditions on one chest scan

Where will AI make its first major market impact in radiology? Four radiology experts share their views at RSNA

Canon debuts AI for image reconstruction and 1.5T MR at RSNA Advanced Intelligent Clear-IQ Engine and Vantage Orian

How will radiologists access AI? Integrating machine learning into existing business structure and radiologist workflow

Aidoc and ACR announce partnership for AI in imaging Establishing a registry to better understand AI in the clinical setting

Arterys touts cloud-native platform and regulatory approval in 98 countries AI capabilities with 'unmatched' security

NVIDIA announces AI partnerships with OSU, NIH Using Clara to deploy algorithms designed in-house

Five key takeaways about AI from RSNA Cutting through the hype to reveal fundamental truths of AI in radiology

Medical training could be facing an AI learning gap

by John W. Mitchell , Senior Correspondent
A prospective review of literature found that for all the buzz around machine learning (ML) and artificial intelligence (AI) in medicine, undergraduate and graduate medical students don’t get much formal training in these emerging tools, suggesting there is currently too much reliance on computer scientists to bridge the ML/AI gap in medicine.

“By the time medical students become researchers or fellows, it’s probably too late,” Dr. Vijaya B. Kolachalama, Ph.D., lead author and assistant professor of medicine at Boston University School of Medicine (BUSM) told HCB News concerning the need for medical ML/AI training.

Story Continues Below Advertisement

RamSoft PowerServer™ RIS/PACS - Enabling Efficient Diagnostic Imaging

RamSoft's PowerServer™ RIS/PACS is an intuitive, single database application that enables healthcare practices to operate diagnostic imaging more efficiently than ever before.Why is this important? Click to find out.



Kolachalama, a computer scientist, said that currently, the practice is for life sciences departments to “borrow” ML/AI expertise rather than to cultivate that expertise as part of medical training. His findings, in which a team widely reviewed published papers through the federal government’s PubMed library, found that the number of papers published in the area of ML/AI has increased since the beginning of this decade. In contrast, the number of publications related to undergraduate and graduate ML/AI medical education has remained relatively unchanged since 2010.

Their findings were just published in the journal NPJ Digital Medicine.

The promising news is that Kolachalama reports that enrollment in an introductory course he has started offering at BUSM in ML/AI is increasing as more students in the medicine, public health, and dental schools are interested in the course.

“Five to ten years ago, it was rare for a computer course to be offered in a medical school,” Kolachalama said. “Medicine is very traditional, but ML/AI is just too useful a tool to overlook. Students are starting to embrace the technology."

He added that he was hired by the BU School of Medicine, in part, to help bring about better training in underlying ML/AI skills for medical students and researchers. He believes that as medical schools can offer successful case studies, ML/AI training for medical students will become part of the regular medical training curriculum.

The authors believe that if medical education begins to implement ML/AI curriculum, physicians may begin to recognize the conditions and future applications where AI could potentially benefit clinical decision-making and management early on in their career. Also, the authors cited the idea that technology without physician knowledge of its potential and applications might increase healthcare costs.

Back to HCB News
  Pages: 1

Artificial Intelligence Homepage


You Must Be Logged In To Post A Comment

Advertise
Increase Your
Brand Awareness
Auctions + Private Sales
Get The
Best Price
Buy Equipment/Parts
Find The
Lowest Price
Daily News
Read The
Latest News
Directory
Browse All
DOTmed Users
Ethics on DOTmed
View Our
Ethics Program
Gold Parts Vendor Program
Receive PH
Requests
Gold Service Dealer Program
Receive RFP/PS
Requests
Healthcare Providers
See all
HCP Tools
Jobs/Training
Find/Fill
A Job
Parts Hunter +EasyPay
Get Parts
Quotes
Recently Certified
View Recently
Certified Users
Recently Rated
View Recently
Certified Users
Rental Central
Rent Equipment
For Less
Sell Equipment/Parts
Get The
Most Money
Service Technicians Forum
Find Help
And Advice
Simple RFP
Get Equipment
Quotes
Virtual Trade Show
Find Service
For Equipment
Access and use of this site is subject to the terms and conditions of our LEGAL NOTICE & PRIVACY NOTICE
Property of and Proprietary to DOTmed.com, Inc. Copyright ©2001-2018 DOTmed.com, Inc.
ALL RIGHTS RESERVED