neal@nairobi ~ bash
$ whoami
Dr Neal Aggarwal — Physician/Surgeon, AI & Robotics Engineer
$ cat focus.txt
Medical AI · LLM Engineering · Agentic Systems · Deep Learning · Algorithmic Trading
$ ls posts/
46 articles — machine intelligence, AI safety, blockchain, medical science
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AI & Medicine All readers ⏱ 32 min

Another Arrow in the Quiver: The Clinical AI Institute Kenya Should Build

AI arrived in Kenyan clinical practice ahead of any plan for teaching clinicians how to use it. A 2025 randomised trial found that physicians who had already completed twenty hours of AI-literacy training still deferred to deliberately erroneous model output — which means the obvious curriculum is the wrong curriculum. This is my blueprint for a permanent national institution that would train and certify the clinical AI competence of an entire country's health workforce: five professional tracks, five gated levels, assessment by simulation and workplace observation rather than attendance, seventy-one core posts specified by qualification, and outcomes published whether or not they flatter us. No such institution exists anywhere in the world.

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AI & Medicine Educators and assessment leads standard settingAngoff

The Angoff Panel for Testing Clinicians

One line on one slide of my Level 1 deck reads: Standard set by modified Angoff panel (No arbitrary 50% pass rate). It is the least glamorous sentence in the whole curriculum and possibly the most consequential. This post unpacks it completely — what a cut score actually is, why 50% is indefensible and norm-referencing is worse, who the borderline candidate is and how you build one, the mechanism step by step with a full worked panel whose arithmetic you can check, what 'modified' really means (the usual story is wrong), what the 2025 meta-analysis of 91 studies says about which variant to choose, and precisely where Angoff stops working.

⏱ 21 min Read article
AI & Medicine All readers OSCEAI-OSCE

One Hidden Error: What an OSCE Is, and What an AI-OSCE Would Be

The blueprint I published last week rests on an examination that does not yet exist. This post explains the machinery it borrows: what an OSCE is, the problem Harden was solving in Dundee when he built the first one, why a written paper can never certify a clinical skill, and what changes when the thing being examined is a doctor's judgement about a machine. Along the way: what conjunctive failure means and why some errors cannot be compensated, why a standardised patient is a trained professional and not a volunteer, and why the pass mark is never 50%.

⏱ 21 min Read article
AI & Medicine All readers AI fluencyclinical AI

Borrowed From an Art School: Where the Clinical AI Framework Actually Came From

The competency framework underneath my clinical AI blueprint was not built for medicine. It came out of an art school in Sarasota and a business school in Cork, developed by a novelist-turned-AI-coordinator and an information systems professor. That provenance is not a curiosity — it is the reason the framework transfers to clinical work at all. A framework built for medicine would have hard-coded medicine into it. This is what Dakan and Feller built, what I kept, what I reframed, and the one thing I had to add because creative work does not kill anyone.

⏱ 11 min Read article
Deep Learning Advanced transformersbackpropagation

The Glass Box Transformer: A Language Model With the Lid Off

Every explanation of a transformer eventually reaches a line like loss.backward() and stops. This is the one that does not. I take a complete decoder-only language model written in NumPy — no framework, no autograd, every gradient derived by hand and checked against finite differences — and walk the whole of it: byte-pair tokenisation, layer normalisation, causal multi-head attention, the residual stream, the backward pass through attention, Adam, nucleus sampling, and the formula that connects a 27,861-parameter toy to a 175-billion-parameter frontier model without changing a single line of the mathematics.

⏱ 45 min Read article
Engineering Advanced hardware hackingmedical devices

A Biohacking Workstation in Your Pocket: Cardputer + BitPirate Against Embedded Medical Devices

A single ESP32-S3 board the size of a business card, running open-source firmware, will speak nearly every wired bus and radio protocol an embedded medical device uses. This is how I use an M5Stack Cardputer ADV with BitPirate as a portable protocol workbench — to map hardware, watch a device boot, sniff its buses and radios, and pull its firmware — worked all the way through with an infusion pump, plus a targeted probing plan. Authorised, isolated, off the patient. The legal and safety constraints are not an appendix here; they are the first tool you pick up.

⏱ 30 min Read article
AI & Medicine All readers medical softwareKenya

The Law Is Part of the Architecture: Building Medical Software for Kenyan Facilities

A third of my care-home specification did not survive one question: under which country's law does this run? Kenya now has a health-specific digital statute, a certification regime for the software itself, and a 72-hour breach clock. This is the work I do — building clinical software for Kenyan care homes and small hospitals that is lawful to deploy, capable of certification, and still standing after contact with the night shift.

⏱ 22 min Read article
AI & Medicine All readers AI in medicineChatGPT

AI Walks Into the Clinic: ChatGPT's Health Launch and the Speed of What's Coming

Two signals landed in the same week: OpenAI wired personal health records into ChatGPT, and a short film — 'AI Doctor 2027' — laid out where this goes. Together they confirm what I have been saying for months: AI is not approaching medicine, it has walked in. My take on both, why the video is worth watching (the graphics alone are remarkable), and what 'augment, not replace' really means once 300 million people bring their chart to a chatbot.

⏱ 5 min Read article
Engineering All readers reverse engineering3D printing

When the Spare Part No Longer Exists: Reverse-Engineering a Medical Machine Back to Life

A working medical machine can be retired by the failure of a single small plastic part the manufacturer no longer sells. This is a service I offer to medical institutions: take a worn or broken component, reverse-engineer it into parametric CAD, and 3D-print a faithful replacement. Here is the full workflow, told through one real part — a filter cap — from caliper to finished print.

⏱ 6 min Read article