Tag Archives: artificial intelligence

Creating Microbiology Presentations Using AI

Confessions of a Microbiologist, Future of Microbiology, , ,

I have been using AI to help create a couple of microbiology-related educational presentations over the past few weeks, for the first time in a couple of years.

I shouldn’t have left it so long…

Things have clearly improved. AI technology is now evolving at an incredible rate. Pause for breath and you will be left behind. It is not perfect by any means but starting to become functional in the professional setting in many different facets, including presentations.

Here are a couple of my personal comments on using AI to generate microbiology presentations for teaching purposes.

  • I used Co-Pilot to generate the presentations: Because I am old and stuck in my ways, I still use PowerPoint for all my presentations. As a result, it makes sense for me to use Co-Pilot, as this is also a Microsoft product and is thus designed to generate PowerPoint presentations. 
  • Prompting is everything: A good initial “prompt ” is key in producing a good AI-generated presentation. How many slides do you want it to produce? Who is the target audience? What areas do you want it to focus on? Do you want it to reference the evidence for its statements? Do you want it to be humorous? Do you want it to include pictures/graphs/tables. Do you want it to include hypothetical case studies. Do you want it to produce MCQs on the topic at the end. And that is just for starters… I think in future I am going to develop a template prompt for presentations which I can then amend for each topic I need to teach on.
  • It still has a tendency to produce generic presentations: My experience so far is that AI has a tendency to produce generic presentations. As I get better at prompting, hopefully I will get better at getting AI to include interesting details in the presentations and not “genericise” so much.
  • Editing is key: The presentation it produces for you will never be exactly the way you want it, even with optimal prompting. Therefore, you will need to dedicate a significant amount of time to editing and polishing it, and indeed giving it a personal touch.
  • Watch out for Hallucinations: In the AI age, hallucinations now have a very different meaning than back in my student days! Large Language Model (LLM) AI systems still have a tendency to make things up if they don’t know the answer. This flaw is becoming less common with more sophisticated software but watch out for it nevertheless. Bias can be another issue. My AI generated presentations did not have any evidence of hallucinations, but there were a couple of clear instances of bias which required editing.
  • It is more difficult to present an AI generated presentation: Because you have not actually written the presentation yourself, it is more difficult to actually present it to an audience. So rehearsal is key. Usually, I would spend 3-4 hours writing a presentation then 1/2 hour rehearsing it. With AI, I would recommend 1/2 hour on the prompt, an hour editing and an hour rehearsing your presentation. AI does save time, but not as much as you might think.

Despite my amateurish initial attempts, I am going to persist with using AI for presentations. The technology can only continue to improve, and very quickly at that. The presentations that it can produce now are light years ahead of what it could do a couple of years ago. I am keen to get tips from anyone else experimenting with giving microbiology presentations by AI. I would also encourage all my colleagues to give it a go.

Several of my children have already been “pinged” at school for using AI in their assignments. Being the bad parent that I am, I ignore the teachers’ frustrations and let the kids embrace the technology as much as they are able. This is the future whether the teachers like it or not…

Michael

 

“Kiestra TLA and the impending Artificial Intelligence revolution”

Future of Microbiology, The Science of Microbiology, , , ,

We are now into our 10th year of having Kiestra TLA at the laboratory where I work in New Zealand. I think it is fair to say that once you have worked in a laboratory with bacterial culture automation (i.e. Kiestra TLA, WASPLab) in place, you would never go back! We certainly don’t intend to.

I am a firm believer in optimising the quality of results generated by the microbiology lab. From a quality perspective, the advantages of automated bacterial culture systems over traditional manual-based methodologies are very impressive.

Here are ten important benefits in terms of quality that result from having a Kiestra TLA in place:

  • Improved Standardization – Automates streaking, incubation, and imaging, reducing variability between technicians and ensuring consistent results.
  • Enhanced Sample Traceability – Uses barcoding and digital tracking to prevent sample mix-ups and ensure a complete audit trail.
  • Optimized Culture Conditions – Automated incubation ensures optimal temperature and humidity, leading to better microbial growth and more reliable colony morphology.
  • Higher Reproducibility – Robotics ensure that plating and streaking techniques are performed identically every time, minimizing human error.
  • Faster Turnaround Times – Automation accelerates the workflow by processing and incubating samples continuously, leading to earlier pathogen detection and reporting.
  • Advanced Digital Imaging – High-resolution imaging captures colony growth at multiple time points, allowing for early detection and remote review without disturbing culture plates.
  • Reduced Contamination Risk – Minimizes human handling of samples, lowering the risk of cross-contamination and false-positive results.
  • Integration with LIS (Laboratory Information System) – Enables seamless data transfer, reducing transcription errors and improving result accuracy.
  • Enhanced Quality Control – Automated processes ensure that each step is performed according to predefined parameters, improving compliance with laboratory standards (e.g., ISO, CLSI).
  • Improved Staff Efficiency and Safety – Reduces manual labor, decreases repetitive strain injuries, and allows microbiologists to focus on complex tasks like interpretation and antimicrobial susceptibility testing.

It is important to note that the list above is Artificial Intelligence (AI) generated. It would take me much, much longer to generate such a list myself! I have however reviewed it and agree with all the points mentioned.

And it is due to the impending AI revolution, that systems such as Kiestra TLA are really going to come into their own over the next 10 years.

The Kiestra TLA system generates thousands of images of cultured agar plates each day, which are ripe for machine learning approaches. AI assisted applications, such as for MRSA identification and identification of urine pathogens are already available on the BD Kiestra platform.

I have no idea what the researchers at BD Kiestra are currently up to (!), but one could envisage that there is a lot of development work going on to further extend these AI-assisted apps into pathogen identification for general wound swabs, sputum samples, etc.

I observe with interest what the Kiestra TLA will be capable of by 2035. One would think that a lot of the routine microbiology culture results will be generated with very little human intervention, leaving the laboratory scientists to focus on the more complex (and interesting) samples.

Undoubtedly, by 2035, we will have new Kiestra TLA hardware in place in our laboratory, but it is in the AI-assisted software where the real revolution is coming…

Michael