4 Ways Vision AI in Healthcare Can Improve Safety, Supply Chain, and Care
I’ve been testing Google AI Studio, and the results have been pretty amazing. Each of these scenarios showcases the power of Vision AI, technology that enables machines to interpret and analyze visual data much like humans, but faster and with remarkable precision.
Vision AI in Healthcare has the potential to revolutionize patient care, improve safety, streamline workflows, and enhance clinical efficiency.
How Vision AI in Healthcare Could Transmore Patient Safety
1. Home Safety Risk Assessments in Seconds
I uploaded a quick video of a living room and asked:
Prompt: “This is the living room of an 80-year-old female with diabetic neuropathy and glaucoma. Create a falls risk assessment.”
Response: In 12 seconds, it generated a customized 725-word safety assessment, including home modifications and assistive devices recommendations.
I then asked it to assess the same space for an 18-month-old. In just 10 seconds, it produced a comprehensive, age-appropriate safety analysis with recommendations.
➡️ AI could empower older adults, caregivers, and parents to proactively identify fall and safety risks, reducing preventable injuries.
2. Automated Surgical Inventory Count
I shared a video of a hospital inventory supply room and asked it to:
Prompt: “Inventory the surgical packs on this cart.”
Response: It said eight. I explained that there were seven. Then, it politely pointed out the one my over 50-year-old eyes had missed, tucked on its side on a darker bottom shelf in less than 10 seconds.
➡️ Improving inventory accuracy could help reduce waste, prevent supply shortages, and streamline workflows—critical for patient care and efficiency.
3. Identifying Surgical Supplies & Their Uses
I uploaded an image of a surgical back table and asked:
Prompt: “Tell me what you know about these supplies.”
Response: It identified all the items, provided both standard and commonly used names, explained their purpose, suggested possible procedures they would be used for, and we even engaged in a discussion about latex content and OR recycling afterward.
➡️ This could be invaluable for new staff learning surgical supplies and instruments, as well as for tracking supply usage—an ongoing challenge for most hospitals.
4. AI-Powered Wound Assessment
I uploaded a de-identified leg ulcer image and provided context that the patient had diabetes.
Prompt: “What can you tell me about this image?”
Response: With disclaimers and warnings about not being able to provide a diagnosis, it still correctly flagged the likelihood of it being a diabetic ulcer, emphasized its seriousness and considerations, and urged seeking medical care.
➡️ Chronic wounds aren’t just a $50 billion healthcare crisis; they can cause pain, loss of mobility or worse, and other significant challenges for patients and families. AI could help patients recognize early signs of skin breakdown and risks, prompting timely intervention before severe complications develop.
Bonus: AI-Powered Estate Sale Inventory
While not healthcare-related, this example is still worth sharing. My family is preparing for an estate sale, so I uploaded a video of one of the rooms, zooming in on smaller items like jewelry, and asked:
Prompt: “Create a spreadsheet of items in the video for an estate sale. List the name, description, and estimated price.”
Response: In under 8 seconds, it generated a detailed inventory with item names, descriptions, pricing ranges, and selling considerations.
The Future of AI in Healthcare & Beyond
These results are just a few examples and came from readily available Gemini models, yet they already show the incredible potential of how tools like Google AI Studio and other Vision AI models can assist in fall prevention, wound care, and supply chain workflows.
Beyond testing, Google AI Studio allows for building and fine-tuning models, opening the door for AI-driven solutions tailored to healthcare’s unique challenges.
Now imagine what could be achieved if healthcare experts fine-tuned these models with high-quality, unbiased datasets and real-world clinical insights.
How do you see AI shaping healthcare?
Disclaimer: These examples are exploratory and showcase potential opportunities. AI models require rigorous validation and real-world testing before clinical use. Tools like Google AI Studio are not inherently HIPAA-compliant and would require additional safeguards when handling Protected Health Information (PHI) in healthcare applications.
