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artificial intelligence

Artificial Intelligence in Radiology: What Every Physician Should Know

Teaser: 

D'Arcy Little MD CCFP FCFP FRCPC,

D’Arcy Little, MD, CCFP, FCFP, FRCPC, Medical Director, Journal of Current Clinical Care and www.healthplexus.net, Radiologist, Orillia Soldiers’ Memorial Hospital, Adjunct Clinical Lecturer, Departments of Medical Imaging and Family Medicine, University of Toronto, Toronto, ON.

CLINICAL TOOLS

Abstract:
Artificial intelligence is rapidly being integrated into radiology practice, with over 1,000 AI-enabled devices now authorized by regulatory agencies worldwide. For referring physicians, understanding AI’s role in medical imaging is increasingly important, as these technologies affect report turnaround times, diagnostic accuracy, and clinical workflows. This article provides a practical overview of current AI applications in radiology, including automated detection of critical findings like pulmonary embolism and intracranial hemorrhage, enhanced cancer screening, and workflow optimization. AI tools assist radiologists in identifying abnormalities that might be missed, quantifying disease burden, and prioritizing urgent cases. However, AI augments rather than replaces radiologist expertise, and all findings require human verification. Future applications will extend beyond diagnosis to include predictive analytics, personalized imaging protocols, and integration with other clinical data sources. Physicians should understand that while AI enhances radiology services, it does not eliminate the need for appropriate clinical context in imaging requests or careful correlation of imaging findings with patient presentation. As AI adoption accelerates, collaboration between referring physicians and radiologists remains essential for optimal patient care.

Key Words: Artificial Intelligence (AI), radiology workflow, diagnostic accuracy, clinical integration.
AI enhances but doesn’t replace radiologists—Over 750 AI-enabled radiology devices are now approved, but they serve as assistive tools that flag findings for radiologist review rather than making final diagnoses. All AI findings require human verification and expert interpretation.
Faster detection of critical findings—AI algorithms can identify urgent conditions like pulmonary embolism, intracranial hemorrhage, and pneumothorax within seconds, enabling prioritization and reducing time-to-diagnosis for critical findings by 30-50% in some institutions.
Improved cancer screening performance—AI assistance increases mammography screening detection rates from 92% to 97%, helping identify small cancers that might otherwise be missed and providing consistent attention to every image.
Clinical context remains essential—Despite AI capabilities, detailed clinical information on imaging requisitions is still crucial, as AI analyzes image patterns but doesn’t understand clinical context the way radiologists do when interpreting findings.
Continue providing comprehensive clinical history—AI doesn’t diminish the importance of detailed clinical information on requisitions. Your description of symptoms, relevant history, and specific clinical questions directly impacts the radiologist’s ability to provide useful interpretations, even with AI support.
Don’t assume all findings are AI-detected—Most radiology reports don’t specifically mention AI use. The radiologist synthesizes information from multiple sources to create comprehensive interpretations, so maintain the same level of clinical correlation with imaging findings as you always have.
Communicate directly for complex cases—If imaging findings don’t match your clinical expectations, contact the radiologist directly. This person-to-person communication remains valuable for complex cases regardless of AI involvement and ensures optimal patient care.
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Related Terms: Radiology, artificial intelligence, radiology workflow, diagnostic accuracy, clinical integration
Disclaimer: 
Disclaimer at the end of each page

Electronic Health (eHealth) Solutions for Low Back Pain—The Present and The Future

Teaser: 

Dr. Eugene Wai 1 Dr. Pavel Andreev2 Alexander Chung3 Greg McIntosh, MSc4 Dr. Hamilton Hall, MD, FRCSC,5

1 is an associate professor in the Division of Orthopaedic Surgery at the University of Ottawa and is cross- appointed to the School of Epidemiology and Public Health. He is head of the University's Adult Spinal Surgery Program and is the medical lead for the region's ISAEC program. His research interests involve regional and systems-based strategies to improve physical activity in back pain.
2is an associate professor at the Telfer School of Management. His doctoral studies centered on the impact of information and communication technologies on activities such as telemedicine and e-learning. His current research program is developing methodologies that enhance healthcare practitioners care delivery.
3 is a PhD candidate at the Telfer School of Management. His research focuses on the use of behaviour change theories to anchor the design of digital technologies. Specifically, he is interested in designing systems to facilitate habit formation for users.4 completed his Masters in Epidemiology from the University of Toronto's Faculty of Medicine. He is currently the Director of Research Operations for the Canadian Spine Outcomes and Research Network.5is a Professor in the Department of Surgery at the University of Toronto. He is the Medical Director, CBI Health Group and Executive Director of the Canadian Spine Society in Toronto, Ontario.

CLINICAL TOOLS

Abstract:Electronic Health (eHealth) technologies for back pain care, including websites and mobile apps, are rapidly growing. Unfortunately, the clear majority are unregulated and not considered credible. Given this growth, clinicians require the tools to help their patients navigate through the "wild west" of options towards more trustworthy platforms. Artificial Intelligence and digital technologies anchored in behaviour change theories have the potential to further transform these eHealth platforms.
Key Words: Electronic Health (eHealth) technologies, back pain care, websites, mobile apps, artificial intelligence.

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www.cfpc.ca/Mainpro_M2

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The Canadian Agency for Drugs and Technologies in Health (CADTH) has published a summary for users entitled "Can you trust Dr. Google," and it recommends that users look at the Author, Date (current), Objectivity, Purpose, Transparency and Usability.
Clinicians should become familiar with several credible eHealth resources to recommend to patients when assisting with their self-management of back pain.
Electronic Health platforms have the potential to engage patients in the self-management of their back pain.
Most available eHealth options for back pain are considered unreliable and not credible; however, several government and professional societies are beginning to publish reliable and useful content for patients.
Standardized tools and principles exist for the appraisal of credible eHealth resources.
Artificial Intelligence and anchoring mobile health solutions in behaviour change theories may further improve eHealth platforms.
To have access to full article that these tools were developed for, please subscribe. The cost to subscribe is $80 USD per year and you will gain full access to all the premium content on www.healthplexus.net, an educational portal, that hosts 1000s of clinical reviews, case studies, educational visual aids and more as well as within the mobile app.
Related Terms: Back Health, Electronic Health (eHealth) technologies, back pain care, websites, mobile apps, artificial intelligence
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The Future of Wheelchairs: Intelligent Collision Avoidance and Navigation Assistance

The Future of Wheelchairs: Intelligent Collision Avoidance and Navigation Assistance

Teaser: 

Pooja Viswanathan, BMath, MSc Candidate, Department of Computer Science, University of British Columbia, Vancouver, BC.
Jennifer Boger, MASc, Research Manager, Intelligent Assistive Technology and Systems Lab, Department of Occupational Science and Occupational Therapy, University of Toronto; Toronto Rehabilitation Institute, Toronto, ON.
Jesse Hoey, PhD, Lecturer, School of Computing, University of Dundee, Dundee, Scotland; Toronto Rehabilitation Institute, Toronto, ON.
Pantelis Elinas, MSc, PhD Candidate, Department of Computer Science, University of British Columbia, Vancouver, BC.
Alex Mihailidis, PhD, PEng, Assistant Professor and Head of Intelligent Assistive Technology and Systems Lab, Department of Occupational Science and Occupational Therapy, University of Toronto; Toronto Rehabilitation Institute, Toronto, ON.

Mobility and independence are essential components of a high quality of life. Although they lack the strength to operate manual wheelchairs, most physically disabled older adults with cognitive impairment are also not permitted to use powered wheelchairs due to concerns about their safety. The resulting restriction of mobility often leads to frustration and depression. To address this need, the authors are developing an intelligent powered wheelchair to enable safe navigation and encourage interaction between the driver and his/her environment. The assistive technology described in this article is intended to increase independent mobility, thereby improving the quality of life of older adults with cognitive impairments.
Key words: mobility, artificial intelligence, assistive technology, wheelchairs, cognitive impairment.

Related Terms: Neurology-Movement Disorders, artificial intelligence, assistive devices, cognitive impairment, mobility, wheelchairs