Wearable Cameras Allow AI to Detect Medication Errors

A team of researchers says it has developed the first wearable camera system that, with the help of artificial intelligence (AI), detects potential errors in medication delivery.

In a test whose results were published today, the video system recognized and identified, with high proficiency, which medications were being drawn in busy clinical settings. The AI achieved 99.6% sensitivity and 98.8% specificity at detecting vial-swap errors.

The findings are reported Oct. 22 in npj Digital Medicine.

The system could become a critical safeguard, especially in operating rooms, intensive-care units and emergency-medicine settings, said co-lead author Dr. Kelly Michaelsen, an assistant professor of anesthesiology and pain medicine at the University of Washington School of Medicine.

"The thought of being able to help patients in real time or to prevent a medication error before it happens is very powerful," she said. "One can hope for a 100% performance but even humans cannot achieve that. In a survey of more than 100 anesthesia providers, the majority desired the system to be more than 95% accurate, which is a goal we achieved."

Drug administration errors are the most frequently reported critical incidents in anesthesia, and the most common cause of serious medical errors in intensive care. In the bigger picture, an estimated 5% to 10% of all drugs given are associated with errors. Adverse events associated with injectable medications are estimated to affect 1.2 million patients annually at a cost of $5.1 billion.

Syringe and vial-swap errors most often occur during intravenous injections in which a clinician must transfer the medication from vial to syringe to the patient. About 20% of mistakes are substitution errors in which the wrong vial is selected or a syringe is mislabeled. Another 20% of errors occur when the drug is labeled correctly but administered in error.

Safety measures, such as a barcode system that quickly reads and confirms a vial’s contents, are in place to guard against such accidents. But practitioners might sometimes forget this check during high-stress situations because it is an extra step in their workflow.

The researchers’ aim was to build a deep-learning model that, paired with a GoPro camera, is sophisticated enough to recognize the contents of cylindrical vials and syringes, and to appropriately render a warning before the medication enters the patient.

Training the model took months. The investigators collected 4K video of 418 drug draws by 13 anesthesiology providers in operating rooms where setups and lighting varied. The video captured clinicians managing vials and syringes of select medications. These video snippets were later logged and the contents of the syringes and vials denoted to train the model to recognize the contents and containers.

The video system does not directly read the wording on each vial, but scans for other visual cues: vial and syringe size and shape, vial cap color, label print size.

"It was particularly challenging, because the person in the OR is holding a syringe and a vial, and you don’t see either of those objects completely. Some letters (on the syringe and vial) are covered by the hands. And the hands are moving fast. They are doing the job. They aren’t posing for the camera," said Shyam Gollakota, a coauthor of the paper and professor at the UW's Paul G. Allen School of Computer Science & Engineering.

Further, the computational model had to be trained to focus on medications in the foreground of the frame and to ignore vials and syringes in the background.

"AI is doing all that: detecting the specific syringe that the healthcare provider is picking up, and not detecting a syringe that is lying on the table," Gollakota said.

This work shows that AI and deep learning have potential to improve safety and efficiency across a number of healthcare practices. Researchers are just beginning to probe the potential, Michaelsen said.

The study also included researchers from Carnegie Mellon University and Makerere University in Uganda. The Toyota Research Institute built and tested the system.

The Washington Research Foundation, Foundation for Anesthesia Education and Research, and a National Institutes of Health grant (K08GM153069) funded the work.

Chan J, Nsumba S, Wortsman M, Dave A, Schmidt L, Gollakota S, Michaelsen K.
Detecting clinical medication errors with AI enabled wearable cameras.
NPJ Digit Med. 2024 Oct 22;7(1):287. doi: 10.1038/s41746-024-01295-2

Most Popular Now

Personalized Breast Cancer Prevention No…

A new telemedicine service for personalised breast cancer prevention has launched at preventcancer.co.uk. It allows women aged 30 to 75 across the UK to understand their risk of developing breast...

New App may Help Caregivers of People Ge…

A new study by investigators from Mass General Brigham showed that a new app they created can help improve the quality of life for caregivers of patients undergoing bone marrow...

An App to Detect Heart Attacks and Strok…

A potentially lifesaving new smartphone app can help people determine if they are suffering heart attacks or strokes and should seek medical attention, a clinical study suggests. The ECHAS app (Emergency...

A Machine Learning Tool for Diagnosing, …

Scientists aiming to advance cancer diagnostics have developed a machine learning tool that is able to identify metabolism-related molecular profile differences between patients with colorectal cancer and healthy people. The analysis...

Fine-Tuned LLMs Boost Error Detection in…

A type of artificial intelligence (AI) called fine-tuned large language models (LLMs) greatly enhances error detection in radiology reports, according to a new study published in Radiology, a journal of...

DeepSeek-R1 Offers Promising Potential t…

A joint research team from The Hong Kong University of Science and Technology and The Hong Kong University of Science and Technology (Guangzhou) has published a perspective article in MedComm...

Deep Learning can Predict Lung Cancer Ri…

A deep learning model was able to predict future lung cancer risk from a single low-dose chest CT scan, according to new research published at the ATS 2025 International Conference...

New Research Finds Specific Learning Str…

If data used to train artificial intelligence models for medical applications, such as hospitals across the Greater Toronto Area, differs from the real-world data, it could lead to patient harm...

'AI Scientist' Suggests Combin…

An 'AI scientist', working in collaboration with human scientists, has found that combinations of cheap and safe drugs - used to treat conditions such as high cholesterol and alcohol dependence...

Patients say "Yes..ish" to the…

As artificial intelligence (AI) continues to be integrated in healthcare, a new multinational study involving Aarhus University sheds light on how dental patients really feel about its growing role in...

Brains vs. Bytes: Study Compares Diagnos…

A University of Maine study compared how well artificial intelligence (AI) models and human clinicians handled complex or sensitive medical cases. The study published in the Journal of Health Organization...

Philips Foundation 2024 Annual Report: E…

Marking its tenth anniversary, Philips Foundation released its 2024 Annual Report, highlighting a year in which the Philips Foundation helped provide access to quality healthcare for 46.5 million people around...