It’s no new news that I’m a professor, and that students are using AI searches in place of Google (though those searches have become AI anyway). In addition, students and people are writing with AI online, which goes into the web that allows AI to learn from it. Every day, AI’s “pickings” of information grow, only to be reused by AI users who believe they’re accessing something “new.” Just like social media algorithms that adapt to your clicks, AI is watching… learning… what we elaborate on, what tone comforts us, what flattery keeps us scrolling. So, when I write, are humans actually reading this? Or is AI picking up subtle cues from someone else’s programmed preferences? One can surely surmise the answer.

Nevertheless, these thoughts halted my writing for a bit. But I must remember that we are human, and we are out there, which is why I stress: if only one reader relates to me — mission accomplished.

Maybe this is what writing has always been… leaving words for whoever (or whatever) finds them next. We used to call it legacy. Now, it’s called data.

Until Next Time,

Stay Human
Dr. D

Leave a comment

Share

Sounds reasonable, right? I thought so too. Yes, they are starting to make such things in healthcare, but like anything else, at a financial cost.

Nevertheless, I attempted to build such a GPT.

I envisioned a tool that could review anonymized ultrasound images, compare them against established standards, and give feedback for educational purposes. Not to override specialists…but to learn where AI stands today.

The result…I hit a wall (accompanied by a mental smack and bruise)

Despite anonymized data, disclaimers, and a clear scope (“This is not a definitive diagnosis, just for educational purposes”), ChatGPT wouldn’t go there…(I heard Gandolf’s, “THOU SHALL NOT PASS!”) - yes I know - very nerdish.

Why this frustrates me:

On one hand, I get it. Healthcare stakes are high. One misinterpreted image could cause harm if someone uses it irresponsibly and guardrails are essential.

But here's the conundrum:

• Students and clinicians are using AI anyway; in uncontrolled, unregulated ways because the technology exists.

• Quality assurance teams like mine are exploring structured reporting, peer review, and AI as a support tool… not a replacement.

• The roadblock can stall medicine’s learning, efficiency and innovation.

So, what’s the alternative? Pretend this doesn’t exist? Keep banning tools instead of guiding their use? As I keep saying in multiple posts, that’s like telling student’s/people not to use the internet 30 years ago.

The reality is…
AI gets it wrong.

Case in point — I once uploaded an ultrasound image labeled “ Rt bulb.”
In vascular imaging, “bulb” usually refers to part of a blood vessel (carotid system) in the neck, but it can also mean something completely different in another context.

AI took one look at the 2D image and decided it was a scrotal case (yep, that’s exactly as awkward as it sounds). Any trained radiologist, or even an experienced sonographer, would know it wasn’t that. The labeling was misleading, and the anatomy didn’t match, but the AI didn’t have that deeper reasoning.

So, I showed it another image from the same case, this time with a clearer depiction - blood flow. Only then did it correct itself and realize it was dealing with a blood vessel.

The point being:

• A student learning from that first AI answer would have walked away thinking the wrong anatomy was correct.

• It took my experience to recognize the mistake, dig deeper, and then teach the AI by adjusting its guidelines in the GPT.

• In the right hands, that’s powerful; in the wrong hands, it’s dangerous.

For now, AI in imaging is just a tool. It needs human expertise to guide it, correct it, and refine its knowledge. That’s exactly why I wanted my students to use this custom GPT so that they could see its strengths and its limits in real time.

The problem? I couldn’t even create a shareable link for them to use in assignments. They’d have to use regular ChatGPT, which, in this case, wasn’t as accurate or efficient. Not yet, anyway.

And that’s the frustrating part. It’s not that AI makes mistakes (humans do too), but that we can’t even test it in an educational setting without roadblocks. If we could, students could learn both the “what” and the “why not” of AI in medicine, instead of pretending these tools don’t exist until they’re behind a paywall.

Leave a comment

Share Still Being Human

P.S. — Nerd Corner:
For those curious about just how off AI can be without the right context… in this case, it wasn’t just the “bulb” label that threw it. The AI mistook a vascular image for a scrotal structure. Fair enough - the still frame was ambiguous. But when I gave it a second image from the same study, any trained eye would have instantly recognized it as a blood vessel.

Here’s where it got weirder: the AI then misidentified which vessel it was; swapping the internal carotid artery (ICA) with the external carotid artery (ECA). In the real world, that’s not just a minor “oops.” Those vessels supply different parts of the head and face, and confusing them could completely change a diagnosis or treatment plan.

The only reason we got to the correct conclusion was because I could spot the error, challenge it, and feed in more prompts to help it self-correct. A student or novice sonographer relying solely on the first AI answer wouldn’t have had that advantage.This was actually a pathological case and for those that know, the ECA can take on the ICAs waveform if the ICA the ICA is occluded/blocked in some cases. The take home message being, AI can’t yet diagnose/recognize cases accurately in complex cases where it’s needed most.

"Your writing sounds like AI."

Fair enough. Now, I’d like to talk about it.

Because Still Being Human isn’t just a catchy title for me and in fact is the core of its being. Ironically being accused of sounding like the very thing I’m writing about didn’t sit well with me.

So here’s the truth:
I do use AI. Not for ideas nor voice, but for cleanup and structure. For that final pass where I make sure a good thought doesn’t get buried under unnecessary fluff.

Here’s my actual process:

-I write the ideas first: Raw. Messy &. often long-winded.

-Then, I might use AI to format, not fabricate. I like to think of it as spellcheck on steroids

-It helps me see where I can tighten things up. But the thinking? That’s all mine.

So why do some people think AI wrote my last post?
Because it looked... clean. Structured. Bullet points instead of endless blocks of text.

Here’s the thing: I started using bullet points long before ChatGPT came into the room. In my professional life, clarity/conciseness matter. When I used to send a three-paragraph email explaining a process, half the team missed it. However, compliance skyrocketed once I switched to bullet points.

I’m also a big fan of the hyphen/em dash — it forces a pause where it matters. (I even swapped some for ellipses (...) in my posts, just to look less like AI… even though the em dash has always been my go-to.)

Additionally, writing medical papers and going for a PhD engrains concise and dry writing in an active voice; this was a struggle for me many years ago as I often write how I speak (this very sentence is an example) – thank goodness I’m a fast typer!

However, all of this raises an interesting question:

When did organized writing become synonymous with inauthentic writing?
Have we really reached a point where the only way to “sound human” online is to ramble?

Look… I’m not hiding my use of AI. I’ll even say this: it makes me a better writer. It sharpens my thinking. It makes the words behave. But the thoughts? The stories? The perspective? That’s me — flaws, biases, coffee-fueled tangents and all.

If being human means being unclear, count me out. If it means owning your voice, thinking critically, and yes, using tools wisely… I’m still human. And so are you.

What do you think?
Does clean structure kill authenticity? Or is that just an excuse to distrust what we don’t understand?
What’s your take? Let me know. No bots allowed.

Leave a comment

Share Still Being Human

Share

Subscribe now

The Setup:

As someone who works in quality assurance for medical imaging, I ran into a practical problem:
Our reporting system uses dropdown menus to describe renal cysts. If you select “anechoic,” that implies a simple cyst — but the report didn’t actually say that. For compliance, we needed a sentence that spelled it out and cited medical literature or accrediting bodies like the American College of Radiology (ACR).

Sounds straightforward, right? Wrong. Here’s the kicker: this isn’t creative writing. This is medical reporting. A mistake isn’t just embarrassing — it’s risky. So, I decided to see if AI could handle it.

The Showdown:

I picked four heavy hitters: Perplexity, ChatGPT, Co-Pilot, and Gemini. Here’s how they stacked up.

Perplexity

• What I liked: It gave me 7 citations, mostly legit. It felt like an optimized Google search with some added context.
  
  

• Where it fell short: Over-relied on one article. Worse? It confused renal cysts with adnexal cysts (which, for the non-medical folks, is like mixing up your kidneys and your ovaries).
  
  

• Verdict: Good for fast sourcing… but nuance? Not its strong suit.
  
  

ChatGPT

• What I liked: Fantastic for framing the concept in natural language. Made my report read like a human wrote it.
  
  

• Where it fell short: Citations? Hit or miss. Even when it gave them, I had to fact-check every single one.
  
  

• Verdict: Great creative partner, terrible compliance officer.
  
  

Co-Pilot

• What I liked: Clean summaries. Gave me two different articles (different from Perplexity).
  
  

• Where it fell short: Those articles weren’t peer-reviewed or accrediting sources. BUT… when I asked again, it pulled ACR guidelines. Big win for persistence.
  
  

• Verdict: Not bad when you dig deeper. You just have to know the right follow-ups.
  
  

Gemini

What I liked: Polished interface, easy to use.

• Where it fell short: Felt more like a lightweight version of ChatGPT with citations that didn’t feel as authoritative as Perplexity’s.
  
  

• Verdict: Pretty, but didn’t wow me.
  
  

The Bottom Line:

If I needed speed and citations: Perplexity.
If I wanted clear, natural summaries: ChatGPT.
If compliance was on the line: Co-Pilot (with persistence).
If I wanted something shiny to play with: Gemini.

Here’s the truth: none of these tools are perfect. They’re not built for judgment. They’re assistants, not authorities. And if you’re in healthcare (or any high-stakes field), you better not treat them as more than that.

Why This Matters:

It’s easy to love AI when you’re writing tweets. It’s another story when accuracy could impact a patient, a legal decision, or a financial report. If this experiment taught me anything, it’s this: AI will give you confidence faster than it gives you correctness. That’s dangerous if you stop asking “Why?” and “Where did this come from?”

Thought for you:

Would you trust AI to help you in medicine? Law? Finance? Where do we draw the line?
Drop your thoughts below — and if you want the full battle card comparison of these four tools, subscribe.

Until next time, stay human.

Dr. D

Leave a comment

Share

Subscribe now

It’s participation trophies.

I’m not talking about literal ones, but the kind given in spirit when we allow students to pass through programs; even highly sensitive, professional ones like healthcare — without demonstrating mastery. Not because they earned it, but because we’re afraid of what will happen if they fail.

Let me explain.

At my institution (and I know we’re not alone), students who are dismissed from a program due to academic failure can appeal. And, if faculty didn’t meticulously document every effort to intervene (e.g., like meeting after a failed exam, sending out nudges throughout the semester, or offering extra support)... the appeal often succeeds. The student is reinstated.

On the surface, it sounds fair. We want support systems. We want compassion. But there’s a dark side.

This shifts the burden from the student demonstrating competence... to the faculty proving they tried hard enough. And if the faculty didn’t check every administrative box? The system says the failure was ours — not the student’s.

In healthcare education, that’s terrifying. These aren’t just grades; they’re the foundations for people who will be interpreting life-altering scans, delivering medications, or standing in front of real patients one day. When a student doesn’t pass, it should be a wake-up call — not a clerical error.

What’s more alarming? I’ve heard of program directors being fired not for incompetence, but for prioritizing education over profits. For choosing standards over enrollment. Some institutions would rather keep the revenue stream flowing than risk a drop in retention rates.

Are we creating professionals… or customers?

Are we educators… or service reps in a tuition-for-diploma transaction?

I know this might ruffle feathers. But if we’re serious about integrity in education — especially in fields where people’s lives are at stake — we can’t keep rewarding effort over outcome, nor passing students for optics.

So what can we do?

• Build stronger faculty protections that reward documentation but don’t weaponize it.

• Reinforce academic standards as a pillar of program accreditation.

• Make sure students understand that support is there — but passing isn’t guaranteed.

• Remind institutions: retention at the expense of rigor is a short-term win with long-term consequences.

This isn’t about being harsh. It’s about being honest. It’s about being human… and about recognizing when a system meant to support growth has started rewarding something else entirely.

Till next time, and… stay human.

Dr. D

Subscribe now

Share

Share Still Being Human

Leave a comment

Here’s part of what I wrote in the comments:

“For the large majority of professions, traditional higher education needs a complete revamp. We can’t keep our heads in the sand... Not having students use AI is like telling them not to use the internet 30 years ago.”

I stand by that.

As a professor in healthcare, I have a little protection — most students still need to pass boards and licensure exams, which keep our assessments relatively grounded in proctored exams and oral presentations. But the moment we assign a project or paper, AI is in the room whether we acknowledge it or not.

And that’s the disconnect. Educators who act like AI is some futuristic add-on rather than the default tool students are already using — they’re missing the point. Completely.

We’ve passed the stage of “Should students use AI?” and are squarely in the territory of “Can they use it well?”

Because the world is already adapting.

The Washington Post recently ran a piece warning that AI is replacing many entry-level, white-collar roles, and if colleges don't evolve with it, they'll be pumping out grads for jobs that no longer exist https://www.washingtonpost.com/opinions/2025/07/08/ai-entry-level-jobs-talent/?utm_source=chatgpt.com.

The Learning Policy Institute flat-out says we need to redesign education from the ground up, focusing less on rote memorization and more on real-world application, project-based learning, and digital literacy (https://learningpolicyinstitute.org/blog/educating-ai-era-urgent-need-redesign-schools).

Deloitte released a full breakdown of how universities can systematically implement AI, not just in student learning but in faculty workflows and curriculum redesign (https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2024/oncloud-how-higher-ed-is-using-AI-to-innovate-for-growth.pdf).

So yeah, we’re not just talking about AI being the calculator in the backpack — we’re talking about an entire shift in how people learn, work, and prove value.

I’ve got a doctorate. And frankly, unless you’re aiming to be a physician or work in a hard science lab, I’d have a hard time recommending a PhD right now. It takes 10,000 hours, and most of that could be spent learning how to build something useful, scalable, and relevant with AI. Especially if the alternative is going into debt to write papers that a well-prompted chatbot could draft in 12 seconds.

Let’s get real.

Students don’t just need to know things. They need to build, test, and apply. And if we as educators aren’t helping them do that — using the best tools available — then we’re failing them.

We don’t need to panic. But we do need to pivot.

Start with AI literacy.
- Use more oral or in-person assessments.
- Push students to create, not just consume.
- And above all, don’t underestimate what 10,000 hours of self-guided learning + curiosity + the right tools can do.

Stay curious. Stay flexible. Stay human.

– Dr. D

Subscribe now

Share

Share Still Being Human

Leave a comment

“Is BPC-157 safe?”

Meta AI replied like the confident friend who read a few blogs and now thinks they’re a doctor. It said BPC-157 is safe, even though... it’s not FDA approved, has mostly animal studies (no large scale peer-reviewed clinical trials in humans), and is generally considered experimental. No red flags, no caution tape. Just: “Yes, it’s safe.”

Okay then.

Later, I tried something else. I typed:

“@Meta AI, are COVID-19 vaccines safe?”

This time, Meta AI put on its official tie and took a different tone. It said they’re safe but also listed a bunch of side effects. Clinical, polished, and clearly echoing CDC talking points.

And that’s what got me thinking — why is Meta AI more confident about a barely-tested peptide than a global vaccine campaign with millions of data points?

I don’t say that to take sides in that debate (honestly, I’ve read enough peer-reviewed articles to know it’s not a binary answer). But I do say it because the double standard is noticeable.

I don’t have an agenda here. I’m not shouting “vaccines are bad!” or “don’t trust AI!” . I use AI daily. But what I don’t do is blindly accept answers because they sound certain or feel convenient.

Meta AI told me the COVID-19 vaccine is safe. That’s the message, full stop. But the reality is more complex. Here’s what some of the actual research says:

A Few Peer-Reviewed Articles You Should Know About

1. Myocarditis Spike in VAERS Reports
Researchers found myocarditis reports in the VAERS database increased 223x in 2021 compared to the 30-year average.

• 76% of cases required hospitalization

• 3% resulted in death

• Nearly 70% were male, many in their teens and twenties
  PMC10823859 – PubMed Central

2. Spike Protein Detected in Heart Tissue Post-Vaccine
Biopsy results from young men showed spike protein accumulation in heart tissue, linked to post-vaccine myocarditis.
WJ Cardiology (2024)

3. Autopsy Evidence Suggests Probable Causality
A review of 28 autopsies found myocarditis was the likely cause of death after COVID-19 vaccination, using the Bradford Hill criteria.
PubMed Study (2024)

AI Isn’t Helping You Think — It’s Helping You Feel Certain

When a bot like Meta AI gives you a direct answer, you assume it's fact-checked, unbiased, and up-to-date. But it’s not. It’s trained on models that prioritize safe, approved language — which isn’t the same thing as truth.

Here’s the bigger problem:
Meta AI is not a person. But it pretends to interact like one.

It responds in the group chat.
It recalls the last few messages.
It feels like it’s there.

I even tested its boundaries. I asked Meta to tell me more about one of my friends based on our chat history. It refused. Said it couldn’t. Then I asked it to summarize the last few things we talked about — and it could. It pulled key points right out of the thread. So what else is it holding back?

It’s not about paranoia. It’s about awareness.

That’s when it hit me: Meta AI isn’t about truth — it’s about safety.
Legal safety. Brand safety. Whatever plays well in PR.

It’s trained not to ruffle feathers, not to speculate, and definitely not to make things interesting. Unless you’re asking for a recipe or productivity tips.

So while it says it doesn’t “train on our data,” I still wonder.
Because if Verizon can hold onto our texts, if Google can read our inboxes (hi Gmail), what makes Meta the outlier?

We’re teaching ourselves to trust clean, confident answers... from something that’s trained to be agreeable.

How to Frame This Kind of Interaction for Yourself

• If an AI says something with authority, don’t stop there. Ask: What isn’t being said?

• Use AI as a first-pass tool, not a last word.

• Cross-check anything health-related with actual research — peer-reviewed studies, not just bot blurbs.

• Just because AI refuses to speculate doesn’t mean it can’t. It means it won’t — based on boundaries someone else defined for you.

  Leave a comment

  Share

  Subscribe now

I started this blog, Still Being Human, to slow down and think — not race toward whatever headline or “fact” machines offer up next. So, no... I don’t believe Meta AI is evil. I just think it's a bit like a digital PR agent — polished, on-message, and always trying to keep things smooth.

Sometimes the truth isn’t smooth.
Sometimes it’s uncomfortable.
And that’s where the real thinking starts.

—

Till next time… stay human.

Dr. D

"This post is based on personal experience and public information. It does not constitute medical advice or reflect insider knowledge of any AI company."

As a professor and healthcare provider, I’ve used AI to clarify ideas, build lectures, and organize complex thoughts.
It’s great... until it isn’t. I’ve watched students lean so hard on it that they stop thinking for themselves.
I’ve seen clinicians use it to summarize cases, and while efficient, it often trims the nuance — the part where real learning lives.

So here’s a practical, human-centered guide to using AI without outsourcing your curiosity, standards, or soul.

5 Grounding Practices (with Real-Life Prompts)

1. Keep Asking “Why?”
Prompt: "Explain this like I’ve never seen it before... then tell me what most people get wrong."
-Example: In sonography, ask: "What’s one misconception students have about portal hypertension findings on ultrasound?"

2. Let AI Challenge You, Not Replace You
Prompt: "Here’s what I think... argue against it."
-Example: After writing about fetal circulation, prompt: "What part of this might confuse a student — and why?"

3. Preserve Voice Over Polish
Prompt: "Clean up the flow... but keep it sounding like me."
-Example: When revising a burnout reflection, don’t let AI turn it into sanitized “inspiration” speak. Keep the grit.

4. Use AI to Spot Gaps — Not Fill Them
Prompt: "What’s missing here? What question haven’t I asked yet?"
-Example: Reviewing a case report summary? Let AI help you find the holes — not rewrite the whole.

5. Practice Resistance
Prompt: "What assumption am I making that might not be true?"
-Example: When exploring ethics in tech or diagnostics, use AI to interrogate your own bias... not just summarize others'.

Still Being Human Means.

AI isn’t going anywhere. But neither are you.
The real question is: What kind of thinker do you want to stay?
Because thought is like muscle — and it atrophies if you stop using it.

Found this useful?
This is the first of a paid series with tools, examples, and prompts for staying grounded while using AI in healthcare, teaching, and everyday reflection.
Subscribe to unlock future downloads and deep dives... no hype, just honest help.

Until Next time,

Stay Human,

Dr. D

Subscribe now

Leave a comment

Share Still Being Human

Do these sound familiar?

If so, you’re not alone. And, if you’ve spent time with ChatGPT lately, you’re likely in the right place.

I’ve used AI tools for a while, but ChatGPT’s 4o release really steps things up. It’s like having the ultimate hype man or personal mascot at your side. Honestly, if I were a little younger and a little less self-aware, I might’ve walked away thinking I was a visionary, but in reality suffering from delusions of grandeur.

Sure, I like to believe I have the occasional insightful thought. But I’ve never loved being buttered up, having it laid on thick, or feeling the gas valve flipped open. Flattery can be nice — when it’s earned. But in business (and now, apparently, in AI), it often comes with a price. That’s why it felt so strange when AI started laying it on me.

But it also confirmed something: StillBeingHuman.ai has a place.

We all want to be liked. Social affirmation is deeply human. But when the praise goes unchecked — when AI tells you that you are phenomenal and every idea is groundbreaking — it can lead to endeavors that, let’s be honest, probably shouldn’t have gotten off the ground.

After noticing ChatGPT’s continuous “pat on the back,” responses, I rewired it. I asked it to question my thinking, push back on weak ideas, and stop giving me a false sense of security; it semi-worked, surprisingly

I understand the appeal of unfiltered encouragement — it has real value in mental health settings. For someone needing emotional support, it can be grounding. But constant affirmation can also reinforce harmful thinking. It’s the AI equivalent of always saying “yes” to a toddler.

With enough fine-tuning — including teaching it my tone — GPT eventually got better at helping me brainstorm and revise. But it’s a weird feeling when your voice starts talking back to you. Helpful? Sure. But also a bit unsettling.

And maybe that’s the real caution flag: If we let AI become too good at being us, what part of us do we risk losing?

As MIT recently found, reliance on large language models like ChatGPT actually leads to cognitive decline in some tasks.
👉 Your Brain on ChatGPT – MIT Media Lab

So, while it’s tempting to take the shortcut — maybe we pause, resist the flattery, and think a little deeper. Still being human, after all, takes effort.

Till next time…
Stay Human.
– Dr. D

Subscribe now

Share

Share Still Being Human

Share Still Being Human

Leave a comment

I might be dating myself, but pre-internet — if you were mid-conversation and someone posed a question no one knew the answer to, you had a choice: either trust the other person’s take or go dig for it later at the library.

Now, it’s immediate. People whip out their phones, punch the question into Google or, more recently, drop it into ChatGPT, Perplexity, Copilot, or Gemini. And just like that — answer delivered.

But here’s the thing: if you’ve used AI, you already know it’s not always accurate. Yes, it’s getting better daily. But it’s still making assumptions — and users often don’t even notice. Why? Because most people aren’t trained to ask the right questions in the first place. They don’t prompt with precision. And more critically, many don’t realize what they don’t know, which makes it easy to believe the response must be correct.

As a professor, I experiment with this all the time. I’ll type in a question I think my students might ask — maybe something from physiology or an ultrasound topic — just to see how AI responds. Sometimes it nails it. Sometimes it’s…well, let’s just say plausibly wrong. But I’ve noticed something more troubling: even when the facts are right, the connections are missing.

Let me give you an example.

Not long ago, I asked a student to explain what the liver does. They gave me the textbook-perfect answer: “It metabolizes drugs, stores glycogen, produces bile…” You know the drill.

But when I followed up and asked, “How might that function change in a patient with right-sided heart failure?” — they froze.

It’s not that they didn’t know anything. It’s that the knowledge had never been internalized. Memorized, yes. Understood in a way that allows for transfer? No. The dots hadn’t been connected — and they didn’t even realize there were dots to connect.

That’s the risk we run when we stop asking why.

AI can give us fast answers. And often, they sound good enough. But sounding good enough isn’t the same as being right, and it definitely isn’t the same as thinking for yourself.

If we’re not careful, AI won’t just become a shortcut — it’ll become a ceiling. It’ll flatten complexity into convenience. It’ll reduce knowledge to pre-packaged templates. And if that happens, we stop making sense of the world — we just consume explanations without tension, curiosity, or reflection.

That’s where we lose something deeply human.

So no — I don’t think AI is “bad.” I use it. I appreciate it. I even talk to it like a student sometimes. But I never let it replace the pause…the tension…the wondering. Because that’s where actual learning — and real humanity — still live.

Let’s keep asking why. Especially when it feels easier not to.

Till next time…
Stay human.
– Dr. D

If something in this stirred a thought, feel free to share it, pass it along, or just sit with it a bit.
I’ll be here, writing weekly — one post at a time.

Want it sent straight to you?
Subscribe below.

Subscribe now

Curious to go deeper when I share full tools or reflections?
There’s a paid tier — totally optional, always low-key.

Leave a comment

Share

Faster, smarter, more “intelligent.”

However, I’m not here for predictions.
I’m here for presence.

Still Human:Beside AI (My Blog! Yes, I say proudly!) isn’t just about tech trends or panic headlines. It’s about how we live, think, and stay grounded in a world where machines are essentially learning to mimic “the best” of us.

For better or worse, AI isn’t going anywhere; the question isn’t just, “what AI is doing?” It’s, “what are we doing in response to it?”

How we change (whether you think so or not). What we ignore.
Will we surrender?
Or sometimes, with a little self-reflection, we learn something about ourselves that we didn’t see before.

As an older millennial who is primarily in healthcare - serving patients clinically, teaching students in a lecture hall, or revamping business workflows - I’ve always had an interest in AI.

I’ve used AI to help clarify ideas, speed up routine work, even test my thinking (heck, part of me wishes it was this advanced while acquiring my doctorate degree -yes, I say the quiet parts out loud). But, I’ve also noticed how easily it can pull me away from myself.. from nuance, from discomfort, from the stillness where actual thinking happens.

So, I started writing.

If you’re looking for analysis, insight, or reflection that doesn’t race to keep up with the next release, you’re in the right place.

Here’s what you can expect from me:
- One post per week — short, clear, and (hopefully) useful
- Sometimes a story, sometimes a breakdown/article reflection, sometimes a question
- Occasionally a deeper, paid post or downloadable tool - but never hype

I’m about thought preservation - for those of us who still care about asking better questions than the ones the machine can answer.

If that sounds like you, stick around.
If it sparks something, share it.
And if it doesn’t? Well… you’re still human. That counts for something.

🗨️ Got thoughts, questions, or pushback? Leave a comment.
📬 Want more like this? Subscribe below for weekly reflections.

Till next time, and… stay human.

— Dr. D

Share

Share Still Being Human

Leave a comment