Aging societies, rising chronic disease rates, and persistent inefficiencies in healthcare are raising fundamental questions about the sustainability of existing healthcare systems. AI has rapidly emerged as the key driver addressing these challenges, with NLP-based generative AI holding transformative potential for healthcare service paradigms.
Large language models (LLMs) that dominated early markets exposed critical problems in medical applications despite their versatility: hallucination phenomena that can lead to misdiagnosis, and cloud-based operations creating patient data security and privacy concerns. As these limitations became clear, Small Language Models (SLMs) specialized for specific domains to maximize accuracy and efficiency have emerged as alternatives.
Market Overview
The global AI healthcare market is projected to grow from approximately $29B in 2024 to $504.1B in 2032 (CAGR 44%). The generative AI healthcare market is expected to grow from $2.92B in 2025 to $84.38B by 2037 (CAGR 31.8%). The SLM market specifically is projected to grow from $6.5B in 2024 to $64B by 2034. Growth drivers include rising chronic disease demand, edge AI proliferation via wearables, and significant computing cost reduction versus LLMs.
Key Applications
Clinical and administrative automation: automatic EHR summarization (Seoul Asan Hospital AI voice recognition), SOAP note generation (Dr.AI), pre/post-consultation process automation. Patient customization and remote monitoring: personal health coaching chatbots (OpenAI+Thrive Global, Seoul "Wrist Doctor 9988+"), wearable integration (SK Biopharm epilepsy device, Apple Watch). Diagnostic assistance: medical image analysis (Lunit, DeepNoid, AIRS Medical), real-time endoscopy polyp detection, AI drug discovery (Isomorphic Labs, $600M investment).
Market Entry Barriers and Strategic Recommendations
Hallucination risks remain even with SLMs; strict medical data regulations limit utilization; complex MFDS approval processes (Korea: 4 approved digital therapeutics vs US: 65). Clinician-patient trust gap persists (86% of clinicians positive about AI improving outcomes; only 60% of patients agree). MenInBlock exemplifies strategic positioning — focusing on low-risk administrative automation rather than high-risk diagnostic areas, using RAG and LTM technologies to establish reliability. Investment trends: 2025 Q1 digital healthcare investment surged 47% to $5.3B, with AI-based startups accounting for 60% of total investment. Government roadmap targets 2x expansion of AI medical technology commercialization 2024-2028, reducing technology gap with the US from 2.7 years to 1 year.
