Medical SLMs vs General LLMs: Why Healthcare Needs Its Own Models

Why general models struggle in healthcare

Large general purpose language models are trained on web scale data. That includes news, social media, code, blogs, and forums. This is excellent for broad language understanding but not enough for medicine.

Clinical work depends on:

• Precise medical terminology and abbreviations.
• Understanding of lab values, units, and reference ranges.
• Knowledge of imaging findings and radiology language.
• Awareness of drug interactions and contraindications.
• Structured schemas such as FHIR and ICD codes.
• Safety rules, red flags, and when to say it does not know.

Studies in journals like Nature Medicine and JAMA have highlighted that general language models can hallucinate clinical facts, misinterpret lab results, or offer unsafe suggestions if used without domain tuning.

• Nature Medicine discussion of medical language models
• JAMA coverage of AI and clinical decision support

What makes a medical SLM different

Medical SLM stands for small language model designed for clinical work. These models are smaller than giant LLMs but are tuned on carefully curated medical data. That focus is their strength.

A medical SLM is built to:

• Use controlled vocabulary and standard terminology.
• Handle messy real world lab reports and imaging summaries.
• Respect safety rails such as never diagnosing or prescribing on its own.
• Work with structured data from labs, vitals, and imaging, not text alone.
• Integrate with standards such as FHIR so outputs are machine usable.

Lancet Digital Health and similar journals show that domain tuned models provide better accuracy and fewer harmful errors on triage and symptom reasoning tasks than generic models with no medical alignment.

• Lancet Digital Health collection on clinical AI

The role of synthetic data in training medical SLMs

Real medical data is sensitive, regulated, and fragmented. You cannot simply pour hospital databases into a training pipeline. Synthetic data helps solve this.

Synthetic clinical sequences allow models to learn:

• How lab values evolve over time for chronic diseases.
• How diagnoses, prescriptions, and tests relate to each other.
• How multi visit histories unfold for real patients.
• What typical and atypical paths look like for common conditions.

Done correctly, synthetic data preserves medical structure while removing patient identity. Journals focused on digital medicine have begun to outline best practices for safe synthetic data generation and evaluation.

Why Aether needs its own medical model

Aether works with data that is messy and unique to India. Lab reports change format from city to city. Imaging summaries vary across centers. Notes may mix English with local languages. Generic models trained on Western datasets do not understand this environment well.

Aether's medical SLM will be grounded in:

• Structured lab data and real report layouts.
• Imaging reports mapped into the health graph.
• Indian clinical vocabulary and abbreviations.
• Health graph relationships across years of patient history.
• Synthetic timelines that represent chronic disease journeys.

The goal is not to answer every general question. The goal is to be reliable and conservative when working with real patient data.

Sources and further reading

• Nature Medicine on medical language models
• JAMA coverage of generative AI in health care
• Lancet Digital Health on clinical AI performance

Information only. Not medical advice. Models must always be used under clinical supervision.

Next steps

• Use Aether primarily as a way to organize and visualize records, not as a replacement for your doctor.
• Watch for future updates where we explain how Aether's medical SLM is tuned and evaluated.
• If you are a clinician, partner with us to define safe, realistic use cases for medical AI in your practice.