AI landscape and models 2025

by with No Comment AI-artificial-intelligenceSite news

AI landscape and models 2025 ,this diagram is a comprehensive 2025 AI model landscape map (originally compiled by Sebastian Raschka). It illustrates how different neural architecture families evolved from transformer-based LLMs into emerging hybrid, recursive, and state-space models (SSMs) — representing the major trends in AI foundation model design from 2021–2025.

1. Decoder-Style Transformers (Top Red Box)

Core lineage: GPT → OLMo → Mistral → LLaMA → DeepSeek → Qwen → Gemma → Kimi → GLM → MiniMax → SmolLM

These represent the mainstream path of modern LLMs based on the decoder-only transformer architecture introduced by GPT in 2018.
Each successive model improves efficiency, reasoning, and multilingual performance.

Key members (chronological order)

  • GPT (2018): Origin of the transformer-based LLM era.
  • OLMo 2 (2024): Open Language Model project emphasizing transparency and open training data.
  • Mistral 3.1 / LLaMA 4 (2025): Highly efficient open-source models optimized for long-context and high reasoning.
  • Gemma 3 / SmolLM 3 (2025): Google and Hugging Face’s lightweight models for edge devices.
  • DeepSeek V3 / R1 (2025): Advanced reasoning LLM with multi-stage training; emphasizes efficiency and reasoning alignment.
  • Qwen 3 (2025): Alibaba’s high-performing multilingual model.
  • Kimi K2 (2025): Focused on bilingual and code reasoning.
  • GLM 4.6 / MiniMax-M2 (2025): Chinese-origin models optimized for dialogue and domain adaptability.

These remain decoder-style transformers — single-direction, attention-based architectures that dominate commercial and open-source ecosystems.

2. Attention Hybrids (Purple Branch, 2025)

Emerging 2025 trend blending transformers with new attention or memory mechanisms for improved long-context reasoning.

Includes:

  • MiniMax-M1
  • Qwen3-Next
  • Kimi Linear
  • DeepSeek V3.2-Exp

These use techniques like:

  • Linear attention / rotary attention optimizations
  • Dynamic context compression
  • Mixture-of-Experts + memory caching

Goal: preserve transformer flexibility while reducing quadratic attention cost and improving interpretability.

State Space Models (Left Pink Branch)

The alternative to attention-based transformers, focusing on sequential processing rather than global attention.

  • S4 (2021): Original Structured State Space Sequence model — efficient for very long sequences.
  • Mamba (2023): Introduced selective state-space updates, enabling longer memory with transformer-like performance but lower cost.

→ Transformer–SSM Hybrids (2024–2025)

Bridging SSMs and transformers:

  • Jamba (2024): Combines Mamba + attention.
  • Samba (2024): Lightweight open model using hybrid mechanisms.
  • Hunyuan-T1, Nemotron Nano 2, IBM Granite 4.0 (2025): Advanced hybrids integrating memory and recurrent capabilities.

These hybridize attention and state-space updates to handle longer context, streaming data, and structured reasoning.

4. Transformer–RNN Hybrids

Recurrent-like LLMs re-emerge:

  • RWKV (2023): Replaces self-attention with time-mixed recurrence — transformer-level quality at lower compute cost.
  • RWKV-7 (2025): Adds gating and fine-tuned recurrence to support multimodal reasoning.

Goal: transformer power with RNN efficiency — better for edge AI and real-time systems.

5. Liquid Foundation Models (2024–2025)

  • LFM 1 (2024) and LFM MoE (2025)
    Use continuous-time dynamics (inspired by liquid neural networks).
    These adaptively change structure based on input flow — promising for robotics, embodied agents, and self-organizing AI.

6. World Models (2025)

  • Code World Mode (2025):
    Integrates symbolic reasoning and world simulation — essential for autonomous agents that simulate possible futures and outcomes.

7. Text Diffusion Models

Extending diffusion architectures (originally for images) into text generation and reasoning:

  • DiffuSeq (2022)
  • LLaDa (2025)
  • Dream 7B (2025)

These generate text through iterative denoising steps rather than direct token prediction — potentially more controllable and less biased.

8. LSTMs and Recursive Models

xLSTM (2024):

Next-gen long short-term memory architecture, merging recurrence with transformer efficiency — suited for small and adaptive models.

Small Recursive Transformers (2025):

A new family featuring:

  • Hierarchical reasoning
  • Mixture of recursions
  • Tiny reasoning submodels

These aim to mimic meta-cognition — reasoning about reasoning — with lightweight, modular recursion layers.

9. General Trends Illustrated

  • 2018–2023: Transformer dominance (GPT to Mistral)
  • 2024–2025: Diversification — hybrids, state-space, diffusion, recursive reasoning
  • Goal: Move beyond static token prediction → structured, efficient, and agentic reasoning

Summary Insight

This map shows how the AI field is transitioning from single-architecture LLMs to a diversified, hybrid ecosystem.
The direction aligns closely with PyDxAI’s design philosophy — modular intelligence built on top of static LLMs but enhanced through memory, reasoning, and dynamic learning, not retraining.

Inside MIKAI: How Retrieval-Augmented Generation (RAG) Makes Medical AI Reliable

by AI-artificial-intelligenceSite news

Artificial intelligence in medicine must never guess.

When a doctor asks a question about a new diabetes guideline or a rare endocrine disorder, the answer must be accurate, transparent, and backed by data.

This is where Retrieval-Augmented Generation (RAG) becomes the foundation of reliability in MIKAI, our evolving medical AI system.

MIKAI’s mission is to learn continuously from trusted sources — guidelines, journals, and local knowledge — while always showing where the information comes from.

RAG is the method that allows this to happen.

⸻

🧠 What Is RAG?

RAG stands for Retrieval-Augmented Generation, a hybrid approach that combines two powerful components:

1. Retrieval — Searching a curated knowledge base for the most relevant documents.

2. Generation — Using a language model (like Mistral, LLaMA, or Magistral) to synthesize a natural-language answer from those retrieved facts.

Instead of relying purely on what’s inside the model’s parameters, RAG adds a real-time “memory” layer — a document store — where verified information is indexed and retrieved when needed.

This is crucial in medical use: guidelines update yearly, research evolves monthly, and each case may depend on local context.

With RAG, MIKAI can stay current without retraining the entire model.

⸻

🩺 Why RAG Matters for Medical Reliability

Traditional large language models (LLMs) like GPT or Mistral learn by pattern recognition — they generate fluent text but can “hallucinate” if the information isn’t in their training data.

In medicine, that’s unacceptable.

If an AI suggests an incorrect insulin dose or confuses a diagnostic criterion, it could cause harm.

In MIKAI, every response — from “best management of diabetic ketoacidosis” to “latest thyroid cancer guidelines” — is backed by retrieved excerpts from the medical literature stored locally and encrypted for security.

⚙️ The MIKAI RAG Pipeline: Step-by-Step

Here’s a simplified version of how RAG works inside MIKAI.

    +----------------+
    |  User Query    |
    +--------+-------+
             |
             v
  +----------+-----------+
  |  Retrieval Component |
  | (Vector DB / RAG DB) |
  +----------+-----------+
             |
             v
  +----------+-----------+
  |  LLM Generator (Mistral,|
  |  Magistral, or Llama)   |
  +----------+-----------+
             |
             v
     +-------+-------+
     | Final Answer  |
     | + Sources     |
     +---------------+

Let’s break down each part as implemented in MIKAI.

⸻

1. Document Ingestion

The ingestion pipeline is where MIKAI learns from trusted data.

Medical sources — PDF guidelines, research articles, textbooks, or hospital documents — are scanned, chunked, vectorized, and indexed.

Example:

When you upload the “2025 ADA Standards of Care in Diabetes”, MIKAI automatically:

• Extracts text using PyMuPDF or LangChain PDF loader

• Splits long paragraphs into manageable chunks (e.g., 512–1024 tokens)

• Embeds each chunk into a high-dimensional vector using SentenceTransformers or InstructorXL

• Stores the vectors in a Qdrant or FAISS database, linked with metadata (title, author, source date)

Each chunk becomes a searchable “knowledge atom” — small, precise, and encrypted

MIKAI’s local setup on Linux (with /opt/mikai/ SSD storage) keeps all ingested documents physically separated from the LLM runtime — ensuring data integrity and portability.

2. 

Retrieval

When a user asks, for example,

“What is the recommended HbA1c target for elderly diabetic patients according to ADA 2025?”

MIKAI doesn’t guess.

The retriever converts this query into a vector embedding and compares it to all stored chunks in the database using cosine similarity.

The top-ranked results (usually 3–5 chunks) are passed to the LLM as context.

This is the “grounding” process — the LLM only generates text based on verified, retrieved facts.

3. 

Generation

Once the context is retrieved, it’s injected into the prompt template.

Answer:

According to the ADA 2025 Standards, HbA1c targets for elderly patients should be individualized.

• Healthy older adults: <7.5%

• Frail or limited life expectancy: <8%

Sources: ADA 2025 Standards of Care, Section 13.

That’s RAG in action — retrieval ensures reliability, and generation ensures readability.

⸻

🔒 Encryption and Security

Medical AI must safeguard data as strongly as it serves it.

MIKAI employs multi-layer encryption across its RAG pipeline:

1. Database Encryption

• All vector stores and metadata in MariaDB/Qdrant are encrypted using AES-256.

• Access keys are stored in a local .env file not exposed via the web tunnel.

2. Transport Encryption

• When MIKAI communicates through a Cloudflare tunnel or API, all traffic is TLS 1.3 secured.

• No raw data or vector payloads are ever sent to public endpoints.

3. Local Sandboxing

• MIKAI runs its ingestion and inference services in Docker containers under –privileged=false mode.

• User-uploaded files never leave the /opt/mikai/ingest directory.

4. Optional Hash Verification

• Each ingested document is SHA-256 hashed.

• On retrieval, MIKAI verifies the hash to confirm that no tampering occurred.

This ensures data authenticity, a core principle for medical compliance and trustworthiness.

🧩 The Memory and Feedback Layer

In addition to the RAG database, MIKAI integrates a memory manager that records interactions and feedback.

Conversations are stored in two layers:

  • Session memory – temporary chat history within the active conversation.
  • Global memory – only high-rated or “approved” responses are promoted here.

This dual memory system lets MIKAI gradually learn from verified human feedback while maintaining strict separation between transient chat and permanent knowledge.

If a doctor flags an answer as correct (feedback = 5), that response is re-indexed into the RAG database — expanding MIKAI’s contextual reliability.

🧩 Example: Endocrine Case Consultation

Let’s imagine a real clinical scenario inside MIKAI’s chat:

Doctor:

A 68-year-old male with type 2 diabetes and mild cognitive impairment.

What is the ADA 2025 recommendation for HbA1c target?

Step 1:

Query embedding → Retrieval from ADA 2025 document store.

Step 2:

Top 3 text chunks retrieved from “Older Adults” section.

Step 3:

Prompt + context fed into Magistral-24B model.

Step 4:

Generated response (grounded in sources) displayed in the chat UI.

Step 5:

Doctor clicks 👍 “reliable” → stored into global_memory.

Later, another user’s query on the same topic retrieves both the ADA citation and MIKAI’s own verified explanation — forming a dynamic, ever-improving knowledge graph.

💽 Continuous Ingestion and Update

Medical science evolves daily, and MIKAI’s ingestion pipeline is built for continuous learning.

Every week or month, new PDFs or journal summaries can be placed into /opt/mikai/new_docs/.

RAG Reliability Metrics

To quantify reliability, MIKAI tracks several internal metrics:

  • Context precision: How many retrieved chunks are relevant
  • Answer faithfulness: Whether the LLM introduces unverified claims
  • Source transparency: Whether all statements cite retrievable sources
  • User feedback scores: Average confidence rating from doctors

For example, MIKAI’s current test on ADA-based queries yields:

MetricScore
Context precision94%
Faithfulness97%
Source transparency100%
User confidence4.8 / 5

These results show how retrieval + encryption + human feedback together make RAG trustworthy in clinical environments.

🌐 Deployment: From Local to Cloud-Linked

MIKAI primarily runs locally on Linux, with GPU acceleration via Tesla P40 and an RX 580 display card.

However, through Cloudflare Tunnels, it can safely expose a mini chat interface to the web for remote testing.

The system’s modular architecture keeps critical components separate

This separation supports high performance, strong privacy, and quick debugging when new models or sources are added.

⸻

🧭 The Philosophy: Reliable AI Through Grounded Knowledge

RAG isn’t just a technique — it’s a philosophy.

For medical AI like MIKAI, reliability doesn’t come from bigger models alone.

It comes from:

1. Grounded data – each answer built upon verified context.

2. Transparency – every citation traceable.

3. Security – encryption and local control.

4. Adaptability – continuous ingestion and feedback learning.

In this sense, MIKAI is more than a chatbot — it’s a digital medical librarian fused with a reasoning engine.

It remembers, retrieves, reasons, and respects confidentiality — the same way a good physician treats knowledge and patient trust.

The Future of Medical AI: Transforming Healthcare in the Age of Intelligent Machines

by AI-artificial-intelligenceSite news

Medical AI is reshaping the way doctors and patients interact with medicine. The integration of algorithms, vast health datasets, and machine learning has brought us closer to an era where AI becomes a true partner to human clinicians.

What is Medical AI?

Medical AI refers to the use of machine learning algorithms, natural language processing (NLP), and advanced data analytics to analyze health information and assist in clinical decision-making. Unlike traditional software that follows predefined rules, AI systems can “learn” from large datasets of medical records, images, lab results, and even real-time patient monitoring devices.

The goal is not to replace doctors, but to augment human intelligence, reduce errors, and improve efficiency. By handling repetitive tasks and analyzing vast volumes of information quickly, AI enables physicians to focus on what they do best: caring for patients.

Key Applications of Medical AI

1. 

Medical Imaging and Diagnostics

AI has achieved remarkable accuracy in detecting diseases from medical images. Algorithms trained on thousands of X-rays, MRIs, or CT scans can identify subtle patterns often invisible to the human eye. For example:

  • Detecting lung nodules in chest CT scans for early lung cancer diagnosis.
  • Identifying diabetic retinopathy in retinal photographs.
  • Spotting brain hemorrhages or strokes on emergency CT scans within seconds.

In some cases, AI systems match or even surpass radiologists in diagnostic performance, especially when used as a second reader.

2. 

Predictive Analytics and Risk Stratification

By analyzing electronic health records (EHRs) and real-world patient data, AI can predict which patients are at risk of complications. Hospitals already use predictive models to:

  • Anticipate sepsis before symptoms fully develop.
  • Identify high-risk cardiac patients.
  • Forecast readmission rates, helping hospitals allocate resources more efficiently.

Such predictive insights allow preventive interventions, potentially saving lives and reducing costs.

3. 

Drug Discovery and Development

Traditional drug development is costly and time-consuming, often taking more than a decade. AI accelerates this process by:

  • Analyzing biological data to identify promising drug targets.
  • Running virtual simulations of molecular interactions.
  • Predicting potential side effects before clinical trials.

During the COVID-19 pandemic, AI helped researchers rapidly scan existing drugs for possible repurposing, demonstrating its real-world utility.

4. 

Virtual Health Assistants and Chatbots

AI-powered virtual assistants can guide patients through symptom checking, appointment scheduling, medication reminders, and even lifestyle coaching. For example:

  • A diabetic patient may receive personalized reminders to check blood sugar.
  • A post-surgery patient might get daily follow-up questions to track recovery progress.

When integrated with EHRs, these assistants become even more powerful, providing context-aware advice.

5. 

Natural Language Processing in Medicine

Much of medicine is buried in unstructured data—physician notes, discharge summaries, or academic journals. AI-driven NLP tools can:

  • Extract key information from clinical notes.
  • Summarize patient histories automatically.
  • Enable better search and knowledge retrieval for doctors.

This reduces documentation burden and makes critical information accessible at the right time.

6. 

Robotics and AI-assisted Surgery

Robotic systems already assist surgeons in precision tasks. With AI integration, these robots can learn from thousands of prior surgeries to provide real-time guidance, reduce tremors, and enhance surgical accuracy. Surgeons remain in control, but AI acts as a co-pilot.

Benefits of Medical AI

  1. Improved Accuracy – Reducing diagnostic errors, one of the leading causes of preventable harm.
  2. Efficiency – Automating routine tasks frees up doctors’ time.
  3. Personalization – Tailoring treatments to genetic, lifestyle, and environmental factors.
  4. Accessibility – AI tools can deliver medical expertise to underserved or rural areas.
  5. Cost Savings – Earlier diagnosis and efficient resource allocation reduce healthcare costs.

Challenges and Limitations

Despite its promise, medical AI faces important challenges:

  • Data Privacy and Security: Patient data is sensitive; robust safeguards are essential.
  • Bias in Algorithms: AI trained on biased datasets may produce inequitable outcomes (e.g., underdiagnosing minorities).
  • Regulation and Validation: Medical AI must undergo rigorous clinical validation before adoption.
  • Integration with Clinical Workflow: Doctors may resist tools that disrupt established routines.
  • Trust and Transparency: Physicians and patients need explainable AI, not “black box” decisions.

These challenges highlight the importance of developing AI responsibly, with both ethical and clinical considerations in mind.

The Human-AI Partnership

The question often arises: Will AI replace doctors? The answer, for the foreseeable future, is no. Medicine involves empathy, context, and judgment that machines cannot replicate. Instead, the most powerful model is a collaboration where AI handles data-heavy analysis, while doctors bring human insight, compassion, and ethical decision-making.

A practical vision is:

  • AI as the assistant – suggesting diagnoses, flagging anomalies, or offering treatment options.
  • Doctor as the decision-maker – validating insights, considering patient values, and making the final call.

Together, this partnership enhances both safety and patient care.

Building MIKAI: The Journey of Developing a Doctor’s Own AI Language Model

by AI-artificial-intelligenceSite news

Artificial intelligence has moved from the realm of science fiction into our daily lives, from virtual assistants on our phones to sophisticated diagnostic systems in hospitals. But the real power of AI lies not only in global corporations but also in the hands of individuals and small teams who dare to build something personal, purposeful, and transformative.

This is the story of MIKAI — short for Medical Intelligence + Kijakarn’s AI — a custom-built large language model (LLM) designed not by a tech giant, but by a practicing doctor who wanted to bring the future of medical knowledge into his own clinic.

⸻

Why Build My Own LLM?

The motivation behind MIKAI began with a simple but pressing reality: modern medicine evolves at an overwhelming pace. Every month, hundreds of new clinical studies, guidelines, and case reports are published. No single human can possibly read them all, much less apply them efficiently to patient care.

Commercial AI systems, like ChatGPT, are useful but limited:

• They lack up-to-date knowledge in rapidly advancing fields like endocrinology.

• They are black boxes with no control over how data is handled or filtered.

• They cannot be customized deeply for specific workflows in a private clinic.

As an endocrinologist, I wanted an assistant who could:

1. Continuously learn from medical corpora, guidelines, and journals.

2. Provide safe, accurate, and evidence-based answers.

3. Integrate with my practice — handling patient documentation, translation, RAG-based search, and structured data management.

4. Evolve under my guidance, not under the roadmap of a distant tech company.

That vision gave birth to MIKAI.

⸻

Early Foundations: From Off-the-Shelf to Self-Built

Like most AI builders, I didn’t start from scratch. The initial steps were exploratory: testing models like Mistral, LLaMA, Falcon, and GPT-NeoX. Each had strengths, but none were tailored for the medical domain.

The first true breakthrough came with Mistral 7B Instruct, running locally on my workstation. I used llama.cpp to deploy it without requiring cloud servers, ensuring data privacy. At this stage, MIKAI was more of a “mini research assistant” than a doctor’s aide, but the potential was clear.

To make the system practical, I introduced Retrieval-Augmented Generation (RAG):

• A document store for medical PDFs, journals, and clinical guidelines.

• A retrieval pipeline that allows MIKAI to quote and reason from real references.

• A separation of chat history vs. global medical memory, ensuring clean, contextual responses.

This architecture laid the groundwork for MIKAI as a knowledge-augmented medical assistant.

⸻

Building the AI Rig: Hardware for a Personal LLM

Running LLMs isn’t just about clever software — it’s also about serious hardware. For MIKAI, I built a custom AI rig that balances affordability with power:

• Dual Xeon CPUs, 64GB RAM for multitasking.

• Nvidia Tesla P40 (24GB VRAM) as the main AI accelerator.

• Radeon RX 580 for display.

• Ubuntu dual-boot with Hackintosh Clover for flexibility.

This setup allows me to experiment with models ranging from 7B to 24B parameters, running quantized versions (Q4/Q5) that fit within GPU memory. On the software side, I use:

• CUDA 12.4 for GPU acceleration.

• Dockerized services for portability.

• MariaDB for structured storage of conversations, tokens, and medical notes.

The result is a doctor’s personal AI workstation — a private lab where I can test, train, and fine-tune models without depending on corporate servers.

⸻

The RAG Layer: Teaching MIKAI to Learn Continuously

One of the core challenges with LLMs is stale knowledge. A model trained in 2023 won’t automatically know the 2025 ADA Diabetes Guidelines or a paper published last week.

That’s where RAG (Retrieval-Augmented Generation) comes in. For MIKAI, I designed a two-layer memory system:

1. Session-based memory — keeps track of conversations for contextual flow.

2. Global medical memory — updated with feedback and curated sources.

Here’s how it works in practice:

• I upload a new guideline PDF (e.g., ADA 2025 Standards of Diabetes Care).

• MIKAI parses it, indexes it into the vector database.

• When I ask a clinical question, MIKAI first retrieves relevant passages before generating an answer.

This means MIKAI doesn’t just hallucinate — it answers with citations and context, much like a real medical resident preparing for rounds.

⸻

From Mini Chat to Doctor’s Assistant

MIKAI’s interface started as a basic local chat. Over time, I expanded it into a multi-functional workspace:

• Mini Chat Widget: Embeddable on websites like doctornuke.com.

• Patient File System: Auto-generates structured medical forms from scanned documents or speech-to-text dictations.

• Multilingual Support: Translates medical guidelines into Thai while preserving technical terms.

• Secure Access: Two-step authentication and Cloudflare tunneling for remote use.

These features transform MIKAI from “just a chatbot” into a practical clinic assistant that handles real workflows.

⸻

Training, Fine-Tuning, and Safety

No medical AI is useful if it’s unsafe. A careless answer can put a patient at risk. That’s why I’ve built MIKAI with multiple safety layers:

• Filtering out unreliable tokens (e.g., scam coins in blockchain experiments, or low-quality sources in medical data).

• Developer blacklists for AI models trained with misleading content.

• Automatic detection of hallucinations by comparing generated answers to retrieved sources.

• Fine-tuning via LoRA (Low-Rank Adaptation) on curated medical datasets.

For larger-scale training experiments, I’m preparing to test Magistral 24B QLoRA — a balance between accuracy and local hardware feasibility (24GB VRAM).

The goal is clear: MIKAI should never give “guesses” in medicine. It must either retrieve evidence, admit uncertainty, or point to guidelines.

⸻

The Challenges Along the Way

Building MIKAI hasn’t been easy. The journey has been full of technical hurdles:

• GPU memory limits: Fitting 20–24B parameter models on a 24GB card requires careful quantization.

• Prompt management: Ensuring clean separation of user queries, context, and RAG inputs to avoid “prompt leaks.”

• Performance tuning: Balancing speed vs. accuracy (tokens per second vs. depth of reasoning).

• UI/UX design: Creating a modern chat interface with session management and retrieval panes.

But every obstacle has also been an opportunity to refine the system.

⸻

Where MIKAI Stands Today

Today, MIKAI is no longer just an experiment — it’s a functioning assistant that helps in real-world tasks:

• Answers complex medical questions with evidence from current guidelines.

• Generates structured medical notes from speech or scanned files.

• Runs privately on local hardware with full data control.

• Supports multilingual translation for medical literature.

• Embeds into websites for sharing knowledge beyond the clinic.

It’s not perfect — but it’s growing, learning, and adapting every week.

⸻

The Future of MIKAI

Where does MIKAI go next? The roadmap is ambitious:

1. Self-Learning LoRA: Allowing MIKAI to continuously fine-tune on newly retrieved data.

2. Medical QA Benchmarking: Comparing MIKAI’s answers against mainstream LLMs for accuracy.

3. Patient Integration: Building a secure, lightweight mobile app for patient-clinic communication.

4. AI Collaboration: Connecting MIKAI with other open-source AI agents (Whisper for voice, Stable Diffusion for visuals, etc.).

5. Scalable Training: Testing larger models (20–30B) with quantization strategies to push accuracy further.

Ultimately, the goal isn’t just to have “my own ChatGPT.” It’s to have a personal, evolving, trustworthy medical partner — one that grows alongside my practice and improves patient care.

⸻

Reflections: A Doctor Building AI

MIKAI is more than just an LLM project. It represents a philosophy of empowerment: that doctors, researchers, and independent builders don’t have to wait for corporations to solve their problems.

We can build our own tools.

We can take control of AI.

We can shape it for real-world needs, not generic use cases.

For me, MIKAI is not the end of a journey — it’s just the beginning. And as it grows, it reminds me daily of why I became a doctor: not only to treat patients, but also to improve the systems that support their care.

The future of medicine won’t be written only in journals or hospitals. It will also be written in the labs, clinics, and laptops of doctors and builders worldwide. And MIKAI is my contribution to that future.

Testing MIKAI Against the Giants

by AI-artificial-intelligenceSite news

Once MIKAI was stable, I ran it side-by-side with GPT-4, Claude 3 Opus, Gemini 1.5 Pro, and LLaMA 70B fine-tuned. I asked them questions from three buckets:

  1. Guideline-based Q&A (e.g., ADA 2025 diabetes standards, AFI workup).
  2. Clinical reasoning (symptoms → differentials → management).
  3. Journal summarization (new NEJM trials, meta-analyses).

Here’s what I found.

Knowledge Depth & Specialization

  • MIKAI 24B
    • Strong recall of guidelines when paired with RAG.
    • Sticks to structured medical language.
    • Rarely hallucinates if context is provided.
  • GPT-4 / Claude
    • Very strong at summarization and general medical knowledge.
    • Sometimes paraphrases or introduces extra details not in the guidelines.
  • LLaMA 70B fine-tuned
    • Competitive with MIKAI, but without RAG it misses clinical nuance.

Clinical Reasoning

  • MIKAI 24B
    • Very good at structured reasoning: protocol-driven answers.
    • Best when the problem is diagnostic or management-oriented.
  • GPT-4
    • Still the king of “Socratic reasoning.”
    • Can explain why one diagnosis is more likely than another.
  • Claude / Gemini
    • Excellent at synthesizing literature evidence to support decisions.

Safety & Reliability

  • MIKAI
    • Needs guardrails for drug dosing.
    • When uncertain, it defaults to “insufficient context” rather than hallucinating.
  • GPT-4 / Claude
    • Safer by design with alignment layers.
    • But often too cautious, producing “consult your doctor” disclaimers (which is redundant for a doctor using the system).