Summary with Keywords
The conversation discusses the differences between AI-driven decision-making in e-commerce and clinical decision-making in healthcare. Key words include:
- *AI in Healthcare*
- *Clinical Decision-Making*
- *Personalized Medicine*
- *Human Agency*
- *Economic Maximization*
[24/10, 09:53]hu2: Is this analogous to clinical decision making:
Key takeaways:
Help Me Decide uses AI to analyze your browsing history (patient's clinical history) and preferences (check out the word preferences in Sackett's classic definition of EBM) to recommend the right product (diagnostic and therapeutic, lab or imaging as well as pharmacological or non pharmacological therapy) for you with just one tap.
The tool helps customers pick the right product, quickly.
(System 2 decision making fast tracked to system 1 and closer to tech singularity)?
Personalized recommendations include clear explanations of why a product is right for you based on your specific needs and preferences.
Personalized precision medicine with explainability to gain trust!
[24/10, 10:28]hu1: The trunk of the protocol is similar in essence, however it's roots and the expected fruits are extreme contrasts.
Both medical decision making and AI mediated "product 🫷 pushing" analyse patterns. That is the common trunk of the protocol.
However, the roots of the medical decision making transaction lie not in economic maximization but rather in humanistic optimization.
A problem is brought to the clinician where the patient has a need to be cured.
Cure by definition implies optimization of some aspect of the patient's agency that enables their life experience. In effect, therefore, the fruit of medical decision making is an enhanced human agency of the patient.
AI based product pushing on the other hand is rooted in economic maximization for the seller, or the platform, and essentially decimates the already weakened agency of choice by not only "suggesting" but "helping decide" the decision to buy, or more aptly enforcing a product "one feels one must buy".
The fruit therefore is a near total erosion of the human agency.
....
Those reading this can decide for themselves on what happens when clinicians become "product pushers"
Thematic Analysis
The discussion highlights the fundamental differences between AI-driven product recommendations and clinical decision-making. Key themes include:
Contrasting Goals
- AI-driven product recommendations are rooted in economic maximization, aiming to sell products or services.
- Clinical decision-making, on the other hand, is focused on humanistic optimization, prioritizing the patient's well-being and agency.
Impact on Human Agency
- AI-driven product recommendations can erode human agency by limiting choices and influencing decisions.
- Clinical decision-making, in contrast, aims to enhance human agency by empowering patients to make informed decisions about their care.
Differences in Decision-Making Processes
- While both AI-driven product recommendations and clinical decision-making involve pattern analysis, the context and goals of these processes are distinct.
- Clinical decision-making involves a deeper understanding of the patient's needs and circumstances, whereas AI-driven product recommendations are often based on browsing history and preferences.
Overall, the conversation highlights the need for careful consideration of the differences between AI-driven decision-making in e-commerce and clinical decision-making in healthcare, and the importance of prioritizing human agency and well-being in healthcare decision-making.
Further conversational learning around the guardrails for ongoing evolutionary attempts to merge system 2 slow descriptive model based thinking trying to know with fast system 1 thinking that just knows:
[27/10, 07:10] hu1: The next patient who walks into a hospital in India might not just meet a doctor. They might also meet an algorithm.
Artificial Intelligence is quietly entering every corner of Indian healthcare from radiology reports to risk prediction, from hospital billing to patient triage. AI is assisting doctors in diagnosis, predicting disease outbreaks, and personalizing treatment plans.
But as the technology evolves, so does the question:
Can AI heal without hearing?
Can it care without crossing the line between precision and privacy?
As India awaits the enforcement of the Digital Personal Data Protection (DPDP) Act, healthcare stands at the crossroads of two revolutions one scientific, the other ethical. Both promise progress. Both demand responsibility.
Read full article: https://lnkd.in/dKKaNBiF
[27/10, 07:25]hu3: Responsibility is key
[27/10, 08:00]hu3: I believe nobody is wrong. It is a perception
[27/10, 08:25]hu4:
Forget it sir. Health for all was to be achieved in 2020..
Immunization is not complete even in 2025
..
..
Doctors are dyeing due to violence from
People
Police
Politicians
and judiciary is *watching* the on the spur of moment attacks on doctors.
See what happens in our courts for a day using online mechanism and you will realize how country is going south day by day and also get answer to question
Hamara desh mahan kyu nahin hain..??
[27/10, 08:48]hu2: Excellent write up on LinkedIn that you linked above 👏👏
One of my questions to begin with on reading this is around quote, "Explainable AI (XAI) is the new necessity — patients and doctors deserve to know how and why a decision was made. A “black box” approach might satisfy speed, but it fails trust."
Interestingly XAI largely currently dwells on the machine explainability layer perhaps as to why the synapses adjusted their weights in the way they did and what was the role of it's prior priming but then I guess there are two more layers to explainability, the individual human layer (fully well accommodated in the care component of the author's CARE AI acronym) and the scientific layer which is currently tackled using EBM empirical evidence analysed by both humans and AI in the loop.
So concerns around communicating, being accountable , respectful and empathic to the individual giving consent also means expecting a reciprocal duty on part of that consenting individual to realise that in healthcare ecosystems, privacy leakage is a trade-off and risk they have to optimise judiciously in collaboration with the caregiving team who will try to ensure scientific usage of their data for the greater good by making it open access after deidentification as in an age old case reports model.
[27/10, 08:49]hu5: The *Alma-Ata Declaration, adopted in 1978*, declared that health is a fundamental human right and called for primary healthcare to be the key to achieving the goal of *"Health for All"* (by 2000).
[27/10, 08:49]hu5: Only the target dates are revised and postponed.
[27/10, 08:54]hu2: Dating is easier than mating! Ouch!
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