Histotechnologists

Apply knowledge of health and disease causes to evaluate new laboratory techniques and procedures to examine tissue samples. Process and prepare histological slides from tissue sections for microscopic examination and diagnosis by pathologists. May solve technical or instrument problems or assist with research studies.

The occupation of "Histotechnologists" currently carries an automation risk of 47.8%, which is slightly below the base risk estimate of 48.4%. This relatively moderate risk is primarily due to the nature of histotechnologists’ work, which involves both repetitive, procedural tasks and activities requiring specialized knowledge and judgment. Many of the workflows in a histology lab are already semi-automated or are candidates for further automation, particularly processes that involve routine handling and preparation of biological specimens for microscopic analysis. However, a significant proportion of the occupation still relies on skills and decision-making that are not fully accessible to current AI and robotic technologies. The top three most automatable tasks within this occupation are: "Embed tissue specimens into paraffin wax blocks, or infiltrate tissue specimens with wax," "Cut sections of body tissues for microscopic examination, using microtomes," and "Stain tissue specimens with dyes or other chemicals to make cell details visible under microscopes." These tasks follow standardized procedures that lend themselves well to automation due to their repetitive and rule-based nature. Advances in laboratory robotics have already seen machines capable of automating embedding, sectioning, and staining processes, reducing human involvement in these areas. As these systems become more reliable and cost-effective, the likelihood of automating these components of the job increases, thereby raising the overall automation risk. Conversely, some tasks remain highly resistant to automation. These include performing electron microscopy or mass spectrometry to analyze specimens, teaching students or other staff, and supervising histology laboratory activities. Each of these responsibilities requires a combination of expert judgment, adaptability, communication, and oversight that current AI systems struggle to replicate. The key bottleneck skills for automation in this field are originality—measured at 2.8% and 2.9%—which reflects the necessity for novel problem-solving and adaptation in response to new or complex cases. These low percentages suggest that while many manual and standardized tasks can be automated, core elements of the profession still require distinctly human input, limiting the risk of full automation.