4. AI in Clinical Decision-Making: Elevating Veterinary Care
Aimed at clinical and non-clinical leaders from veterinary businesses
Veterinary care is becoming more complex, and with advances in diagnostic technology, veterinarians are expected to process a vast amount of data to make informed decisions. This is where artificial intelligence (AI) has the potential to supercharge clinical decision-making. By analysing large datasets, recognising patterns, and providing predictive insights, AI can support veterinarians in delivering more accurate and timely diagnoses, improved treatment plans, and enhanced patient outcomes.
In this blog, we’ll explore how AI is set to transform clinical decision-making in veterinary medicine, improving both the quality of care and operational efficiency. Some of the AI use cases in this blog are not yet available/ of sufficient quality to be used confidently in the veterinary clinical space, but have been included to shed light on what the potential for the future of AI in the veterinary space looks like. The critical element for getting these highly accurate tools created is high quality veterinary data (more on this to follow in future blogs).
The Complexity of Clinical Decision-Making in Veterinary Medicine
Veterinarians are tasked with diagnosing and treating a wide range of conditions, often with limited patient history or incomplete data. The increasing availability of diagnostic tools (such as imaging, blood tests, and genetic screening) provides valuable information, but it can also lead to data overload. Keeping up with the latest research, treatment options, and case comparisons is a challenge for even the most experienced clinicians.
AI addresses this challenge by processing vast amounts of data quickly and accurately, offering veterinarians data-driven insights that can enhance clinical decision-making. Whether it’s diagnosing a complex condition or predicting treatment outcomes, AI tools have great potential and will one-day become indispensable in modern veterinary practices.
How AI Enhances Clinical Decision-Making
AI-Assisted Diagnostics
One of the most significant applications of AI in clinical decision-making is in diagnostics. AI tools, especially those using machine learning (ML), can analyse diagnostic images such as radiographs, and cytology slides, with remarkable speed and, when trained appropriately, accuracy. These tools can detect patterns and anomalies that might be missed by the human eye, providing valuable second opinions and increasing diagnostic confidence. AI systems are also capable of analysing data from blood tests, genetic profiles, and other laboratory results to offer more comprehensive diagnostics.
For example, AI could assist in diagnosing conditions like:
Orthopaedic issues by analysing X-rays for subtle signs of joint or bone abnormalities.
Cardiac conditions through pattern recognition in electrocardiograms (ECGs) and audio recordings of heart sounds.
Tumour identification by reviewing medical imaging for early signs of cancerous growths.
By supplementing the veterinarian’s expertise, AI-assisted diagnostics could reduce the risk of human error and can lead to earlier detection of diseases.
The accuracy of these tests depends on the quality of the data used to train the AI, so even though some tools produce equivocal results currently, there is scope for vast improvements with improved quality of datasets used to create them.
Predictive Analytics for Treatment Plans
AI has the potential to take diagnostics a step further by using predictive analytics to recommend treatment plans. AI tools could analyse patient data alongside historical data from thousands of similar cases, helping to predict outcomes for different treatment options. These insights would allow veterinarians to develop more tailored and effective treatment strategies, increasing the likelihood of successful outcomes.
For instance, AI could predict how a patient might respond to a specific medication or treatment based on their medical history, breed, and condition. This would reduce the need for trial-and-error approaches and ensure that treatments are as targeted and effective as possible. This is still an emerging possibility in the veterinary space; high quality extremely large datasets of clinical data are needed to create high quality tools in this area that are of sufficient accuracy for clinical confidence.
Enhancing Preventive Care
Preventive care is crucial for maintaining long-term animal health, yet it’s often overlooked due to time constraints or lack of data. AI tools in the future could help by analysing a patient’s medical history and identifying potential risks based on patterns in the data. This proactive approach would allow veterinarians to provide targeted preventive care, such as recommending specific vaccines, screening tests, or lifestyle changes before a condition worsens.
AI could also help track a patient’s response to preventive care measures over time, making it easier to adjust recommendations based on real-time data. This would support veterinarians in providing more personalised care and improving long-term outcomes for their patients. We are a little way off having these tools currently, but with access to the right data, they will become a feature in the future.
Decision Support for Complex Cases
In particularly complex or rare cases, AI can act as a clinical decision support tool. By comparing the current case with an extensive database of similar cases, AI could suggest possible diagnoses or treatment options based on what has worked in the past. This would be especially helpful when a veterinarian is dealing with an unfamiliar condition or when multiple diagnoses are possible.
AI systems could also provide access to the latest research and treatment protocols, ensuring that veterinarians have the most up-to-date information when making decisions. This would help to reduce uncertainty and provide a more structured approach to managing challenging cases.Whilst these tools are not yet widely available in the veterinary space, large language models (LLM’s) for example are being used and developed to help curate differential diagnoses lists based on presenting clinical signs and suggest treatment plans, which can be supportive, particularly in less familiar cases and when time-pressures in consults are high. LLMs are also starting to be implemented to provide structure to existing unstructured clinical notes, which will help in looking for an understanding of clinical patterns in disease.
There are some valid concerns over inaccuracies of LLMs, due to hallucinations; to an extent the use of retrieval augmented generation (RAG) can help to mitigate this by focusing on veterinary texts, but risks of errors remain. More robust AI systems built using domain experts in veterinary medicine and causal methodologies whilst also examining vast datasets of pet health data will provide far more robust clincial tools for the future.
AI in Drug Prescriptions and Dosing
AI tools have the potential to assist veterinarians in prescribing the right medications and determining precise dosages for individual patients. By analysing factors such as weight, age, breed, and medical history, and based on analysis of historical medical records of large numbers of patients, AI might be able to recommend the optimal dosage for a specific medication, reducing the risk of under- or over-medication. This could be particularly important in treating chronic conditions or prescribing medications with narrow therapeutic windows.
AI in the future could also help predict adverse drug reactions based on a patient’s previous medical records or breed-specific sensitivities, further improving the safety and effectiveness of treatment.
Streamlining Data Management
Veterinary practices often face data management challenges, with clinical records, diagnostic images, lab results, and client communications scattered across different systems. AI could help consolidate and analyse this information, making it easier to access relevant data when making clinical decisions. For example, AI-powered electronic medical records (EMR) systems could, in the future, automatically flag abnormalities, suggest diagnoses, or recommend follow-up tests based on a patient’s medical history and current symptoms. As previously mentioned, LLMs can already create structured data from unstructured clinical notes, aiding streamlining of data management.
This integration of data would help veterinarians make more informed decisions and ensure that critical information is not overlooked during the diagnostic or treatment process.
Implementing AI in Your Practice: Key Considerations
Start Small
Begin by integrating AI tools into a specific area of your practice, such as diagnostic imaging or electronic medical records. This allows your team to familiarise themselves with the technology and see immediate benefits before expanding to other areas.
Train Your Team
AI is most effective when combined with human expertise. Ensure that your team is trained on how to use AI tools to support clinical decisions rather than replacing their judgement. AI should enhance, not replace, the clinician’s role in providing care. It is essential to consider that the AI can be wrong and that any vets using the technology are critically aware of this and the limitations.
Monitor and Adjust
As with any new technology, it’s essential to monitor how AI tools are performing in your practice. Track improvements in diagnostic accuracy, patient outcomes, and overall efficiency. Use this data to adjust your approach and maximise the benefits of AI.
Conclusion: AI as a Partner in Veterinary Decision-Making
AI has the potential to transform the way veterinarians diagnose and treat patients, offering faster, more accurate insights that lead to better patient outcomes. By supporting clinical decision-making with data-driven insights, AI could help veterinarians provide more personalised and effective care, while also streamlining practice operations.
In our next blog, we’ll explore how AI can be used to optimise inventory management and streamline the day-to-day operations of your veterinary practice.
This blog explains how AI supports clinical decision-making, highlighting its future role in diagnostics, treatment planning, and preventive care, positioning it as a vital tool for improving patient outcomes.