Development

Patients' Affinity for AI Messages Drops if they Know the Technology was Used

In a Duke Health-led survey, patients who were shown messages written either by artificial intelligence (AI) or human clinicians indicated a preference for responses drafted by AI over a human. That preference was diminished, though not erased, when told AI was involved.

The study, publishing March 11 in JAMA Network Open, showed high overall satisfaction with communications written both by AI and humans, despite their preference for AI.

New Research Explores How AI can Build Trust in Knowledge Work

In today’s economy, many workers have transitioned from manual labor toward knowledge work, a move driven primarily by technological advances, and workers in this domain face challenges around managing non-routine work, which is inherently uncertain. Automated interventions can help workers understand their work and boost performance and trust. In a new study, researchers explored how artificial intelligence (AI) can enhance performance and trust in knowledge work environments.

AI may Help Clinicians Personalize Treatment for Generalized Anxiety Disorder

Individuals with generalized anxiety disorder (GAD), a condition characterized by daily excessive worry lasting at least six months, have a high relapse rate even after receiving treatment. Artificial intelligence (AI) models may help clinicians identify factors to predict long-term recovery and better personalize patient treatment, according to researchers from Penn State.

AI can Open Up Beds in the ICU

At the height of the COVID-19 pandemic, hospitals frequently ran short of beds in intensive care units. But even earlier, ICUs faced challenges in keeping beds available. With an aging American population, 11% of hospital stays included ICU stays.

Artificial intelligence (AI) offers a possible solution, says Indranil Bardhan, professor of information, risk, and operations management and Charles and Elizabeth Prothro Regents Chair in Health Care Management at Texas McCombs.

AI Model Predicting Two-Year Risk of Common Heart Disorder can Easily be Integrated into Healthcare Workflow

AFib (short for atrial fibrillation), a common heart rhythm disorder in adults, can have disastrous consequences including life-threatening blood clots and stroke if left undetected or untreated. A new study demonstrates that UNAFIED, a highly accurate artificial intelligence (AI) prediction model which uses machine learning to parse information acquired from a patient's electronic health record (EHR) to predict whether a patient has or might develop detectable AFib

Researchers Find Telemedicine may Help Reduce Use of Unnecessary Health Tests

Low-value care - medical tests and procedures that provide little to no benefit to patients - contributes to excess medical spending and both direct and cascading harms to patients. A research team from Mass General Brigham and their collaborators have found that telemedicine may help to reduce the use of low-value tests. The work is published in JAMA Internal Medicine.

AI Revolutionizes Glaucoma Care

Imagine walking into a supermarket, train station, or shopping mall and having your eyes screened for glaucoma within seconds - no appointment needed. With the AI-based Glaucoma Screening (AI-GS) network, this vision could soon become a reality.

Glaucoma is the leading cause of irreversible blindness in Japan and worldwide. Early detection is critical, as the disease progresses silently, slowly constricting one's peripheral field of vision.

Research and Development News

Patients' Affinity for AI Messages Drops if they Know the Technology was Used

New Research Explores How AI can Build Trust in Knowledge Work

AI may Help Clinicians Personalize Treatment for Generalized Anxiety Disorder

AI can Open Up Beds in the ICU

AI Model Predicting Two-Year Risk of Common Heart Disorder can Easily be Integrated into Healthcare Workflow

Researchers Find Telemedicine may Help Reduce Use of Unnecessary Health Tests

AI Revolutionizes Glaucoma Care

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