Jon Stojan
© Ramanakar Reddy Danda
Large Language Model (LLM):
Advanced AI gave way to Large Language Models, such as Megatron-Turing NLG, capable of executing a huge number of tasks. However, large-scale LLMs come with huge challenges that include high energy consumption, excessive memory, and high computational costs. Because of this, research is shifting toward more efficient alternatives like Small Language Models.
Following are a few examples of LLMs based on their size:
- GPT-4: 1 trillion parameters
- BERT: 340 million
- T5: 11 billion parameters
- Megatron-Turing NLG: 530 billion parameters
- GShard: 600 billion parameters
But the bigger they are, however, the far greater the possibility of inaccuracies or generally unexpected behavior. Enterprises are using these models to enable everything from chatbots to content generation, but as more applications go into production, some of their limitations become clear.
Small Language Model (SLM):
Since LLMs are rapidly increasing, their software deployment on hardware is becoming impossible due to large numbers of parameters. Hence, recently, SLMs have come into action to provide almost the same effectiveness in different tasks with a few parameters. Some of them are:
Microsoft’s Phi-3-mini-4kInstruct: 3.8 billion parameters, trained on synthetically and curated data, optimized for reasoning tasks.
TinyLlama-1.1B: This is a distilled model of Llama 2 with 1.1 billion parameters, fine-tuned on Ultra Chat for conversational applications.
Google Gemma2-2b: 2 billion parameters, excelling in text generation, question answering, summarization, and reasoning.
SmolLM-1.7B by Hugging Face: 1.7 billion parameters, trained on textbooks, stories, and Python code for educational and general tasks.
qwen2-1.5B: 1.5 billion parameters, still performing quite strongly on coding and math
We can think about LLMs and SLMs as points on a spectrum, with some overlap between them. Taken together, SLMs differ from LLMs along one or more of the following dimensions.
SLMs are more fine-tuned because they are trained by vendors or companies on more detailed, domain-specific data-for examples, to support complex data engineering tasks.
They enrich user prompts-for example, by injecting domain-specific data into a user’s question to make a response more accurate.
They augment the output-for example, by having multiple models produce outputs from different sets of data to provide users with more contextual understanding.
Healthcare Use Cases of SLMs (Small Language Models):
Applications using SLMs abound in medical treatment, given the resource efficiency of such techniques and their capability to provide useful support in tasks that vary greatly, especially when data is in a shortage, or other computational resources are highly limited. Some of these are specific healthcare use cases for SLMs.
Virtual Health Assistants: SLMs can be deployed as virtual assistants to handle routine inquiries by patients, like appointment and medication scheduling. These assistants will also be light and hence perform well under conditions when fast automated responses are expected while resources-for instance, server power-should be conserved.
Telemedicine Support: SLMs can support telemedicine by providing doctors with instant answers to patient questions regarding symptoms, treatment plans, or post-consultation care instructions—and that too, quite accurately.
Medical Translation: SLMs will be trained in medical literature, clinical data, and patient records for even the most complicated medical terminologies and concepts to be translated correctly.
Patient Assistance: This would involve appointment setting, basic health advice, and so on, thus freeing up valuable time that the health professional can spend on other aspects while attending to the patients.
Small Language Modal Transformation Healthcare Workflow:
- Automation of Administrative Tasks
SLMs would automate such routine tasks, freeing up more time for clinicians. The key applications include:
Appointment Scheduling: It integrates with the calendar and provides an online booking facility along with confirmations and reminders.
Verification of Insurance: It provides for real-time eligibility checking, handling pre-authorization and generating accurate claims.
Report Generation: Populates reports from EHR data and summarizes the key findings.
Such automation reduces the administrative overheads and increases the efficiency of the clinics.
- Improve Patient Communication
SLM-driven chatbots can facilitate patient communication by:
Being available 24*7 for questions.
Converting jargon into easy, understandable simple language
Providing multi-lingual service capabilities: it bridges the gap between patients and caregivers.
SLMs improve patient health engagement and give way to Satisfaction.
- Clinical Decision Support
SLMs support clinicians to: Analyze data from comparable medical cases to find out emerging trends and patterns.
Suggest probably diagnosis and use predictive analytics to foresee complications or adverse reactions.
These tools support and do not replace clinician judgment to make appropriate decisions.
- Faster Research & Development
SLMs speed up medical research through.
Analysis of literature & identification of promising areas Identifying patterns towards new treatments.
Drafting the grant proposals required to fund the research.
- Patient Care
SLMs can now contribute to personalized medicine by: Risk assessment of the disease, well in advance, when intervention is possible.
Providing recommendations about personalized treatment programs based on patient profiles. Preventive measures as per one’s lifestyle and genetic information.