Medical and Clinical Laboratory Technologists

However, many parts of the job still rely on people. Explaining test results to doctors or patients usually needs a human’s expertise and communication skills. For example, Stanford Medicine made an AI tool that drafts plain-language summaries of lab results for patients, but a doctor always reviews and approves the message before it’s sent ^[2].

Tasks like setting up, cleaning, and calibrating lab machines also remain manual because they require careful handling. Likewise, supervising and training other lab workers needs judgment and teaching ability that AI doesn’t have. In short, AI and machines can handle much of the data entry and routine testing, but medical laboratory technologists are still needed for interpreting results, solving problems, and guiding others in the lab.

On the other hand, trust and safety are very important in healthcare. Experts point out that adopting AI in medicine requires clear rules and confidence in the technology ^[1]. Because lab results affect patient care, hospitals will only use AI tools that are proven reliable.

This means adoption is often gradual. For example, in the Stanford test, doctors still double-check the AI’s work before it reaches patients ^[2]. In short, labs are likely to add more AI where it clearly helps (like speeding data recording or spotting errors) but will keep humans in the loop for critical decisions and communication.

Overall, the human skills of judgment, teamwork, and empathy remain essential even as technology advances.