Artificial intelligence in veterinary medicine poses ethical challenges

The use of artificial intelligence (AI) is increasing in the field of veterinary medicine, but veterinary experts caution that the rush to adopt the technology raises some ethical considerations.

“A big difference between veterinary and human medicine is that veterinarians have the ability to euthanize patients, which could be for a variety of medical and financial reasons, so the diagnoses provided by AI algorithms have a lot at stake. game,” says Eli Cohen, associate. clinical professor of radiology at the NC State College of Veterinary Medicine. “Human AI products must be validated before going to market, but there is currently no regulatory oversight for veterinary AI products.”

In a review for Veterinary Radiology and Ultrasounds, Cohen discusses the ethical and legal issues raised by the AI ​​veterinary products currently in use. He also highlights the key differences between veterinary AI and AI used by human doctors.

Currently, AI is commercialized among veterinarians for radiology and imaging, largely because there are not enough practicing veterinary radiologists to meet the demand. However, Cohen points out that AI image analysis is not the same as a trained radiologist interpreting images in light of an animal’s medical history and unique situation. While AI can accurately identify some conditions on an X-ray, users need to understand the potential limitations. For example, AI may not be able to identify all possible conditions and may not be able to accurately discriminate between conditions that look similar on x-rays but have different courses of treatment.

Currently, the FDA does not regulate AI in veterinary products as it does in human medicine. Veterinary products can be brought to market with no oversight other than that provided by the developer and/or the AI ​​company.

“AI and how it works is often a black box, meaning even the developer doesn’t know how it makes decisions or diagnoses,” says Cohen. “Add to this the lack of transparency from companies in AI development, including how the AI ​​was trained and validated, you are asking veterinarians to use a diagnostic tool with no way to assess whether is it accurate or not.

“Since veterinarians often receive a single visit to diagnose and treat a patient and are not always followed up, AI could be providing misdiagnoses or incomplete diagnoses and a veterinarian would have limited ability to identify that unless the diagnostic is reviewed. case or serious illness occurs. the result is produced,” says Cohen.

“AI is being marketed as a replacement or as having similar value to a radiologist’s interpretation, because there is a gap in the market. The best use of AI in the future, and certainly in this early phase of implementation, is with what’s called a radiologist in the loop, where the AI ​​is used alongside a radiologist, not instead of one,” says Cohen. . “This is the most ethical and defensible way to employ this emerging technology: leverage it to give more vets and pets access to radiologist consultations, but more importantly have domain experts troubleshoot AI and prevent outcomes. adverse effects and harm to the patient.

Cohen recommends that veterinary experts partner with AI developers to ensure the quality of the data sets used to train the algorithm, and that third-party validation tests be conducted before the AI ​​tools are released to the public.

“Almost anything that a veterinarian might diagnose on x-rays has the potential to be medium to high risk, meaning it can lead to changes in medical treatment, surgery or euthanasia, either due to clinical diagnosis or client’s financial limitations,” says Cohen. “That level of risk is the threshold that the FDA uses in human medicine to determine if there should be a radiologist in the circuit. We would be wise as a profession to adopt a similar model.

“AI is a powerful tool and will change the way medicine is practiced, but the best practice going forward will be to use it alongside radiologists to improve access and quality of patient care, rather than use it to replace those queries.

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