Saturday, November 1, 2025

Visual 4 What is Intelligence GIM November 7

What is intelligence?





Animal intelligence vs plant cognition?


Speakers thoughts 
and Rhetoric:

Rhetoric:

There is nothing called artificial or real intelligence.

What exists is just "intelligence" that is embedded in all manifest beings in this Earth right from apparently inanimate plants to animate humans including embedded artificial systems that humans appear to be currently developing because of their own intelligence deficiency.

This human intelligence deficiency (requirement) is primarily memory because of which humans have been cursed with their penchant for dimensionality reduction resulting in asynchronous intelligence aka primordial AI. 

This is the reason modern humans have become more and more academic as academics is largely all about communicating and learning through a two dimensional interface, be it early cave paintings or current day xy axis mobile screens!

More thoughts here:


What is artificial intelligence?

The word artificial is from stem of ars "art" (see art (n.)) + -fex "maker," from facere "to do, make" 

from PIE *ar(ə)-ti- (source also of Sanskrit rtih "manner, mode



Facere and facient etymology:

facere "to make" (from PIE root *dhe- "to set, put").

Entries linking to -facient

*dhē-, Proto-Indo-European root meaning "to set, put."

Dheya Sanskrit: to be held 

https://www.wisdomlib.org/definition/dheya

It might also be the source of: Sanskrit dadhati "puts, places;" Avestan dadaiti "he puts;" Old Persian ada "he made;" Hittite dai- "to place;" Greek tithenai "to put, set, place;" Latin facere "to make, do; perform; bring about;" Lithuanian dėti "to put;"




What is natural intelligence?

An embedded intelligence that exists in all of nature's responsive flora and fauna.

What is the difference between artificial and human intelligence?

Artificial intelligence is human intelligence. What commonly passes off as AI is machine intelligence. 

What is it to be human?

To be human is to be vulnerable. 

What is it to be human centered?

It means one is empathic enough toward other individual humans to be able to solve their problems effectively.

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Visual 5 Then and now! What was clinical decision making like in the pre AI LLM era just few years back and now?

What was clinical decision making like in the pre AI LLM era just few years back?


Video demo of our patient centered, clinical decision making lab: 

Recent re-upload:

https://youtu.be/ZKoljY2UBHI?si=UYUfpTD7JGOgoQhA

Original upload:

https://youtu.be/xvE5b8Xk3vM?si=dqDlPQgA_EP2L7zT


Video demo of a single patient's decision making: 


https://youtu.be/csF8VQbOYRo?si=mlbHXIyD5A-29uqf


What is it like now?


Hands on demonstration of human clinical decision making with AI in the loop:




Explaining the user interface for clinical decision making with AI in the loop:

Image above: Current AI driven clinical decision making workflow as well as user interface and medical cognition engine.

Rhetoric : The above interface has essentially evolved from a felt need toward dimensionality reduction leading to what is aka academic flatlands that hides multiple hidden layers, which can pose issues with explainability at a machine level. 


Image CC licence: https://commons.m.wikimedia.org/wiki/File:Rock_Shelter_8,_Bhimbetka_02.jpg#mw-jump-to-license

Rhetoric: Human animals invented AI beginning with asynchronous intelligence through their ability to use cave painting tech to convert multidimensional real life data into two dimensional data in an xy axis cave wall that later evolved to paper and electronic media so that they could eventually manage their lives better as artistic modelling was easier in a two dimensional virtual plane than a multi dimensional real plane!

Let's look at where we have come all the way from primordial AI (aka asynchronous intelligence) to modern AI that models primordial AI to produce some currently interesting results particularly if the data capture is asynchronously hyperlocal.

Unquote: https://userdrivenhealthcare.blogspot.com/2025/08/udlco-crh-reducing-multidimensional.html?m=1


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Visual 6:layered approach to clinical decision making, GIM, November 7

A layered approach to clinical decision making: 


Explainability, trust and layers of clinical decision making in pre and current AI LLM era:

Machine layer and AI dominance with humans in the loop:

How useful is AI in the loop of humans and how crucial are humans if placed in the loop of AI?


Analytical scientific and EBM layer: This layer is where our clinical decision making lab appears to be largely engaged in although the other two layers are no less important.

We have already shared something around this layer in our previous demos particularly our two video links shared above.

Human layer: This is the most important layer where clinical decision making actually happens at multiple human stakeholder levels:


We are all apprentices in a craft where no one ever becomes a master.
Ernest Hemingway, The Wild Years

Human, Scientific and Machine layers :



Anatomy of cognitive layers:









Physiology of cognitive layers in clinical decision making: enter Bloom's taxonomy!


RUAAEC
ApRUAECAp

More here on the bloom game of learning cognition: https://sites.pitt.edu/~super1/lecture/lec54091/001.htm
Bloom's taxonomy image copyright as well as an enlightening write up: https://www.niallmcnulty.com/2019/12/introduction-to-blooms-

Enter decision trees and the machine layers:


Shukratic Conversational dialogue:

[01/11, 19:51]hu1: @⁨hu2 made this decision tree for cough as presenting symptom. While making this chart, I realised how the decision points, point back to other organ system generalised symptoms to go through a similar decision tree to confirm or rule out a differential.

[01/11, 19:53]hu1: @⁨hu2 @⁨hu3 @⁨hu4
If this is something workable, Im planning to make similar decision tree for all other generalised symptoms.


[02/11, 07:17]hu2: Looks very interesting! Well done 👏👏

The only problem with this "yes no," binary,  reductionist , decision making approach is that the patient will end up trying to answer a lot of questions with 50% probability of accuracy and could be very taxing for the patient!

Our approach to decision making is on the other hand synthetic where instead of asking incisive questions (Bloom's level 4) that could be limited by the limitations of current knowledge (aka limited static ontology), we collect all possible event data and try to synthesize a broader picture (Bloom's level 6) after making the necessary reductionist edits using the Bloom's 4 knife!

Apologies if the above appeared inscrutable because currently I'm looking at whatever data flows into me, in the context of my upcoming November 7 presentation that I shall try to share with you ASAP once ready when it could make more sense!

[02/11, 07:18]hu2: A Herculean task, either way, although perhaps could be made more efficient using ML tools but then ML is limited to what currently humans know and that may not be enough, which you may realise if you start sitting in a real OPD and start seeing real patients!

Hu1 is a medical student who is working as our PaJR clinical decision making volunteer and also working as a research assistant in our collaborative project on building a potential "clinical decision making automated user interface" with IIT Hyderabad. He is possibly one of the rare medical students to have presented a clinical decision making scenario in a medical CPD conference within a month of having just entered medical college! More about him here along with our other team members past and present: https://medicinedepartment.blogspot.com/2021/03/medicine-department-training-programs.html?m=1
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Visual 7: Human clinical decision making with AI in the loop: translating and grappling with human consent

 Human clinical decision making with AI in the loop:


The human layer and Ux interface



  • "Sometimes the smallest things take the most room in your heart." —
  • Winnie the Pooh
  • Above was Winnie the Pooh translating the Chandogya Upanishad:
  • छान्दोग्य उपनिषद् ८.१.३*

    अथ य एषोऽणिमैतदात्म्यमिदं सर्वम्।
    तत् सत्यम्। स आत्मा। तत् त्वम् असि श्वेतकेतो इति।


Smallest events in human lives  are synthesized to create significant events?


In the human context of privacy, can small measures taken daily go a long way to sustain a long term secure health system experience?


How do we deidentify as per HIPAA, the entire data that is captured into our system 2 healthcare data processing ecosystem?

Can missing the smallest things sometimes take up the most room in our workflow?

Are the smallest things, sometimes the smallest pieces in the puzzle, most rewarding in terms of learning and illness outcomes?


Is the work of AI LLMs as just a machine translator in our multilingual workflow small enough?







Consent form: Machine translation provides an added feature to our informed patient consent form that allows a single click translation to any global language!


Let me know if the konkani seems right!

In case it's not we have a manual back up here used routinely for majority of our patients: 


The above is one layer of explainability and raising awareness about patient rights including right to privacy.

Assignment: Get your LLMs to go through the consent forms linked above and check if they are DPDP compliant and if not ask for a better draft of the above consent form to make it DPDP compliant.


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Visual 8: Role of daily events in clinical decision making and role of visual data capture and representation to generate quick human insights and prevent TLDR

Role of daily events in clinical decision making and role of visual data capture and representation to generate quick human insights and prevent TLDR


In a human centered learning ecosystem, with AI in the loop, manual translation is more common?



Above is a layer of manual human to human translation as well as intermittent problems in an otherwise complex patient with comorbidities (will discuss again in the next layer of AI driven analysis)






Again this patient does have comorbidities related to his metabolic syndrome such as heart failure but then intermittent simple human requirements of explainability manifest in his daily sharing through his advocate such as the one here that manifests in his sleep and meta AI helps not just to translate it but also explain it well.

The role of AI driven infographics in explainability:

The role of AI driven infographics in explainability:




Speaker's thoughts: A picture speaks more than a thousand words!

A video can be time consuming though!

Assignment: Ask your LLMs to gather all the patient data from the case report linked above and rearrange it using AI driven removal of exactly dated time stamps and replacement with unidentifiable event timelines comprising labels such as Day 1,n season of year 1,n.





This patient is an example how human simple explainability backed by scientific evidence can provide a new lease of life to a patient of myocardial infarction who travelled the long distance to our college just for that explainability to strengthen his prior trust in us!

Past published work on similar patient: 

LLM textual explanation followed by translation and then text to voice file for the patient's advocate who like most of us also suffers from TLDR:

LLM textual explanation followed by translation and then text to voice file for the patient's advocate who like most of us also suffers from TLDR:





Also demonstrates AI driven support for insulin dose calculation through human learning around carb counting, accounting for insulin correction or sensitivity factor and insulin to carb ratios to decide the total insulin pre meal dose with scientific accuracy.

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Visual 9: The Scientific analytical cutting layer: GIM November 7

The Scientific analytical cutting layer:



What is the sensitivity, specificity of a CT abdomen in a woman with chronic mild intermittent regular pain abdomen and a vague lump in her abdomen?




Are most drug efficacies simply of marginal benefit to patients?


Individual clinical decision making around antibiotic choices anecdote:




Fever chart 

"@⁨Meta AI⁩ Update:
Reviewed the history and it does look like she began with right lower limb cellulitis and then went on to develop heart failure as similar to our ProJR here: @⁨hu1 and then currently she appears to be having nosocomial sepsis and I'm not sure how she grew klebsiella in her blood culture at the day of admission before she was escalated here on piptaz @⁨hu3 please share her deidentified blood culture report.

Unquoted from:


Global clinical decision making around antibiotic choices anecdote:




"It's 3 AM. You're staring at a febrile patient with suspected sepsis. Culture pending. Your hand hovers over the prescription pad. Piperacillin-tazobactam? Meropenem? The voice in your head whispers: "Go broad. Cover everything. Better safe than sorry."

You write for meropenem. Again.

Here's what that voice doesn't tell you, that, in doing so, you've just contributed to a crisis that's killing more people than you might save."


Unquoted above from the link below:

https://www.linkedin.com/pulse/tales-medical-practice-chapter-11-when-antibiotics-stop-kosuru-kknbc


And AI driven decision support for the whole patient:



Above from the static case report journal published version : 



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