Small language models (SLMs) are becoming increasingly popular because they are more efficient and cost-effective than larger language models (LLMs). SLMs are easier to adopt, more energy-efficient, and cheaper to train and deploy than LLMs. They are also more resilient to cyber-attacks and have a smaller carbon footprint. Additionally, SLMs meet regulatory requirements regarding copyright, reliability of data, and data privacy. While not meant to replace LLMs, SLMs offer easier scalability for industry-specific applications. Experts predict SLMs will have a significant impact on the market by 2025. Research from Apple has shown that using FlashAttention, a technology that improves efficiency, and choosing the right hardware and training schemes can further optimize SLMs. This is particularly helpful for institutions with limited resources. In the healthcare sector, economists believe AI can reduce the U.S. budget deficit by improving the efficiency of healthcare services and public health. However, there are challenges in implementing AI in healthcare, such as regulation and incentives.