Dr. Diego Cesar
Lerma Torres

Large language models lack persistent, structured memory for long-term interaction and context-sensitive retrieval. Expanding context windows does not solve this: recent evidence shows that context length alone degrades reasoning by up to 85% - even with perfect retrieval. We propose a bio-inspired memory framework grounded in complementary learning systems theory, cognitive behavioral therapy's belief hierarchy, dual-process cognition, and fuzzy-trace theory, organized around three principles: (1) Memory has valence, not just content - pre-computed emotional-associative summaries (valence vectors) organized in an emergent belief hierarchy inspired by Beck's cognitive model enable instant orientation before deliberation; (2) Retrieval defaults to System 1 with System 2 escalation - automatic spreading activation and passive priming as default, with deliberate retrieval only when needed, and graded epistemic states that address hallucination structurally; and (3) Encoding is active, present, and feedback-dependent - a thalamic gateway tags and routes information between stores, while the executive forms gists through curiosity-driven investigation, not passive exposure. Seven functional properties specify what any implementation must satisfy. Over time, the system converges toward System 1 processing - the computational analog of clinical expertise - producing interactions that become cheaper, not more expensive, with experience.

The integration of Artificial Intelligence (AI) into medicine promises to transform diagnosis and treatment. However, a significant portion of these tools fail to achieve sustained clinical adoption, not because of technical shortcomings, but due to a fundamental epistemological mismatch with medical practice. This article proposes that effective AI design should emulate clinical reasoning —a pragmatic, hierarchical, and optimized process for managing uncertainty with limited resources. We propose a three-phase conceptual framework—Screening, Diagnosis, and Management—that translates clinician logic into design principles for AI systems. It is argued that success lies not in the pursuit of maximum algorithmic accuracy, but in the creation of interpretable tools that integrate seamlessly into the workflow, augment the clinician’s cognitive capabilities, and, crucially, are validated with data representative of the local epidemiological and socioeconomic context. This paper aims to serve as a bridge between AI engineering and clinical epistemology, advocating a co-development paradigm for creating beneficial and safe technology.

Artificial intelligence (AI) promises substantial gains in healthcare, yet Mexico’s adoption remains hindered by a mismatch between sophisticated algorithms and unresolved system fundamentals. This paper presents a clinically realistic, evidence-informed framework that distinguishes between use cases for screening, diagnosis/monitoring, and administrative optimization, arguing that the most significant near-term value lies in logistics and burden reduction. We inventory Mexico’s conditions—significant “data debt” due to fragmented, non-interoperable records alongside underused strategic assets such as national clinical repositories and highly motivated human capital—and translate them into three actionable principles: (1) prioritize digital infrastructure and interoperability before advanced analytics; (2) foster a collaborative, open-source ecosystem and regulatory sandboxes for safe, rapid validation; and (3) design for the “augmented doctor” via explainable AI integrated into clinical workflows. Success should be measured not by algorithmic novelty but by reduced administrative load, improved interoperability, and a resilient digital health ecosystem that enables subsequent AI gains at scale.