Work Smarter, Not Harder: How AI Tools Are Changing the Game in the Clinical Lab

The clinical laboratory is one of the busiest, most documentation-heavy environments in healthcare. Between policies, SOPs, competency assessments, vendor escalations, and the ever-present pile of administrative tasks, it can feel like there aren't enough hours in the day. So, when a tool comes along that can draft a professional email in seconds, generate a quiz questions directly from your SOP, or model the ROI of a new instrument acquisition before your next leadership meeting, it’s hard to not to pay attention.

That tool is AI, and it's already available to you right now.

In a recent MediaLab by Vastian webinar, Dr. Alec Saitman, PhD, DABCC — Associate Professor of Pathology and Laboratory Medicine and Director of Chemistry and Toxicology at Oregon Health and Sciences University — walked clinical lab professionals through a series of practical, real-world AI applications that anyone in the lab can start using today. Across all of it, the highest value of AI in lab medicine lies in workflow efficiency, documentation, and standardization, and the technology is well within reach.

First Things First: What Exactly Is AI?

Before diving into prompts and applications, it's worth grounding ourselves in what we're actually talking about.

Artificial Intelligence (AI) refers to computer systems that perform tasks typically requiring human intelligence — things like learning, reasoning, problem solving, and language understanding. At its core, AI works by identifying patterns in massive datasets and using mathematical algorithms to predict the most appropriate output.

Large Language Models (LLMs) are a specific type of AI focused on language. Think of them as sophisticated text-prediction engines trained on billions of parameters, books, articles, websites, and more. When you type "The patient's sodium level is," an LLM can predict whether the next word is "low," "elevated," or "within normal range" based on everything it has learned about clinical context and language.

Generative AI is the broader category that includes tools capable of creating new content, like text, images, audio, and even video. All LLMs are a form of generative AI, but not all generative AI is an LLM (a tool that generates music, for example, is generative AI but isn't an LLM).

The performance of these systems also improves over time as more data and feedback are incorporated, which is why the field seems to be moving so fast right now.

A Quick Tour of the Major Platforms

You've likely heard some of these names. Here's a brief rundown of the most commonly used AI tools:

ChatGPT (by OpenAI) is perhaps the most widely recognized. It's versatile and conversational, and it handles a broad range of tasks well. It's particularly strong at drafting text, summarizing content, and generating structured outputs like tables and bullet points.

Gemini (by Google) integrates well with Google's ecosystem and, according to Dr. Saitman, has emerged as a strong image generator. It's available as both a consumer tool and through institutional Google Workspace licenses.

Microsoft Copilot (by Microsoft) is deeply integrated with Microsoft 365 products, making it especially useful for organizations already using Word, Excel, Outlook, and Teams. Many healthcare institutions have access through existing Microsoft enterprise agreements.

Claude (by Anthropic) excels at nuanced writing tasks and can adapt to different tones and voices, includingscientific, conversational, and executive-facing content. As Dr. Saitman noted, his team has used Claude for policy and procedure review, and the results have been notably thorough and well-organized.

Each tool has its strengths and trying more than one for the same task can be illuminating. You may find that different platforms give you different (and complementary) outputs.

The Golden Rule: Better Prompts, Better Outputs

There is one principle Dr. Saitman returned to throughout the webinar: the quality of what you get out depends heavily on what you put in.

AI platforms will always attempt to produce an output, but a vague prompt tends to yield a generic result. The more specific and contextual you are, the more useful the response will be.

Consider the difference between:

• "Make a picture of a laboratory scientist in a lab looking at results."
• "Create a realistic image of a clinical lab in a modern academic medical center. The scientist is wearing navy scrubs and a white lab coat and is reviewing critically abnormal potassium and troponin results. Show racks of tubes, reagent refrigerators, and overhead fluorescent lighting."

The first gives you something that looks like a stock photo from a research lab. The second gives you something that actually looks like your lab. The same principle applies to text prompts: be clear about format, tone, audience, length, and purpose, and you'll spend far less time iterating.

That said, iteration is also a legitimate and powerful strategy. AI platforms are designed for back-and-forth — you can ask for something, refine it, make it shorter, change the tone, and build on it progressively. Think of it as a very patient collaborator.

Real Applications for Real Labs

Here are some of the most practical use cases Dr. Saitman demonstrated live during the webinar:

1. Summarizing Interview Notes

After a candidate interview, most of us are left with a jumbled Word document full of stream-of-consciousness bullet points. AI can take those rough notes with typos, abbreviations, and all, and synthesize them into a clean, structured summary with strengths, areas for development, and an overall impression. From there, you can ask it to reformat the summary as an email to your hiring manager and adjust the length with a single follow-up prompt.

What might take 15 minutes of careful writing can be done in under a minute.

2. Drafting Vendor Escalation Emails

We've all been there: five calls to technical support, five different answers, and a PT failure that still isn't resolved. AI won't take the emotional edge out of your frustration, but it can help you channel it into a professional, strategically worded email. You can paste in your raw notes and ask for an assertive-but-professional tone, then dial it up or down based on your relationship with the vendor and the urgency of the situation.

3. Generating SBARs and Clinical Communications

SBAR (Situation, Background, Assessment, Recommendation) formats are a staple in healthcare communication, and they can be time-consuming to build from scratch. With AI, you paste in your raw information, such as a new test being added, a protocol change, or a new collection requirement, and it structures it into a clean, readable SBAR that you can then refine and send to providers or leadership.

This is particularly useful for labs that are rolling out new tests or navigating changes in clinical guidance, such as updating an OGTT protocol in response to changes in OB/GYN preferences.

4. Renaming Test Orders for Clarity

Epic naming conventions, particularly for tests with similar names or algorithms (like syphilis testing), can cause significant ordering confusion. AI can generate dozens of naming options based on your criteria (include terms like "monitoring," "reverse algorithm," "Treponema pallidum first") and help you get to a short list much faster than going back and forth in committee meetings. You can then present those options to your clinical stakeholders for a final decision.

5. Writing Competency Quiz Questions

If you have an institutional (closed) AI platform, you can upload an SOP directly and ask the tool to generate multiple-choice quiz questions based on its content. The results are often surprisingly useful — covering specimen handling, QC procedures, instrument maintenance, and reference range interpretation. Specifying "non-negative questions" (i.e., only one correct answer, no "which of these is NOT..." phrasing) keeps the questions straightforward and effective.

For labs without an internal AI tool, you can still prompt AI to generate generic competency questions for a given test area, such as routine clinical chemistry or urinalysis, without sharing any internal documents.

6. Complex Dilution Protocols

For those working in chemistry or toxicology, AI can help draft multi-step dilution schemes that specify intermediate stocks, solvent volumes, final spike volumes, and organic solvent percentages based on your target concentrations and solubility constraints. As always, verify the math independently, but AI can get you a solid working framework in a fraction of the time.

7. Financial Modeling and ROI Analysis

Want to bring a new test in-house but need to justify the cost? Paste in your current send-out volumes, per-test costs, and instrument pricing scenarios, and AI can generate an executive-ready financial comparison across multiple options — purchase, lease, reagent rental, and status quo. The output often includes a five-year cost-of-ownership analysis, break-even timelines, and even talking points for leadership presentations.

The HIPAA Question and Why It Matters

This is the question every medical laboratory professional should be asking before they paste anything into an AI platform: Is this information safe to share?

The short answer is it depends on which platform you're using and how your institution has configured it.

Most publicly available AI tools (ChatGPT's free tier, standard Gemini, etc.) are open platforms, meaning that the information you enter may be used to train future models. Patient identifiers, institutional data, and proprietary SOPs should never go into these platforms.

Closed or enterprise-configured AI tools, such as Microsoft Copilot deployed through a HIPAA-compliant institutional agreement, or Google Gemini through a managed Workspace environment, operate within your institution's firewall and do not expose your data externally. Many institutions are already deploying these, which opens up a significantly wider range of use cases.

Dr. Saitman's rule of thumb is to keep prompts generic where possible (no patient names, no facility-specific identifiers) and confirm with your IT or compliance team before inputting any internal documents into a non-institutional tool. When in doubt, leave it out.

Where AI Still Falls Short

Balance is important here. Dr. Saitman was candid about where AI is not yet ready to be trusted without significant human oversight:

• Clinical diagnosis based on lab data: AI can provide context and surface reference ranges, but it is not a diagnostic tool. The consequences of an error here are too high.
• Image generation with text: AI-generated images frequently misspell words or render text inconsistently, since letters are generated visually rather than semantically.
• First drafts as final products: Every AI-generated output needs human review. It's a starting point, not a finished deliverable.
• Source of truth for clinical or analytical questions: AI can point you toward relevant literature, but it can also hallucinate citations or misstate guidelines. Always verify against primary sources.

The Bottom Line

AI isn't going to replace the skilled professionals who run clinical laboratories. But labs that thoughtfully incorporate AI tools into their workflows will have a real operational advantage — whether that means faster documentation, better-structured communications, or more time freed up for the work that genuinely requires human expertise and judgment.

Start small. Pick one use case, maybe drafting that vendor escalation email you've been putting off, or generating quiz questions for an upcoming competency, and see how it feels. Most platforms offer free tiers that are more than sufficient for exploring.

And as Dr. Saitman put it: "AI is a powerful tool. It's not a source of truth." Use it accordingly, and you'll find it's one of the most useful additions to your professional toolkit in a long time.

Want to Keep Learning?

This webinar is now available in the MediaLab by Vastian Compliance & CE Library — along with the full slide deck and a Word document containing all of the AI prompts demonstrated during the session. Whether you missed the live event or want to revisit a specific example, it's all there and available for CE credit.

👉 Visit the MediaLab by Vastian Compliance & CE Library to access this webinar and explore hundreds of additional CE opportunities designed specifically for medical laboratory and healthcare professionals.

MediaLab by Vastian is the leading quality management software platform built exclusively for clinical laboratories and healthcare organizations, supporting more than 9,500 sites worldwide — including all top 20 U.S. hospitals and all top 10 clinical labs in the country.