Introduction In system design, we talk endlessly about state-of-the-art models and benchmarks. But when you step away from the IDE and work as a medical examiner analyzing postmortem remains, you ...

Introduction In system design, we talk endlessly about edge cases and failure modes. But when you step away from the IDE and work as a night duty doctor in a geriatric hospital, you realize that h...

Introduction In the previous post, I argued that a search engine must come before the AI model, and I closed with a promise to get specific about the engineering. This post is that promise paid of...

Introduction In the previous post on representation learning, we discussed how to map complex dental data into computing spaces. However, before deploying flashy AI models, we must ask a fundament...

Introduction In the previous post, I discussed why forensic dental AI depends on more than just models. Before a system can learn anything useful, it must first have access to: Structured den...

Introduction In the previous post, I discussed why forensic dental identification cannot rely only on exact queries. A missing person identification system does not operate in a clean database en...

Introduction Most data systems are built around queries. Input a condition Retrieve matching records This paradigm works well when the data is complete and the queries are precise. Howeve...

Introduction Building a system is only half the problem. In forensic identification, correctness is not optional. A system that stores dental records is not useful unless it can be trusted under ...

Introduction In many real-world systems, decision-making is framed as an optimization problem. The objective appears straightforward: Minimize loss Maximize outcomes However, in practice,...

Introduction In the previous post, I explored how dental records can be transformed into structured, queryable data. However, querying alone does not solve the core problem of forensic identificat...