## 5 Future Directions
### Technical Dimension  
With the rapid advancement of LLMs, technical concerns such as scalability, computational power, multimodal data integration, and efficiency in real-world environments have become central issues for the future of this technology. Designing models that can reduce resource consumption while maintaining accuracy and performance is one of the key priorities for future research. Additionally, the ability to understand and combine diverse inputs such as text, image, and audio has introduced new challenges in semantic alignment, multimodal architectures, and strategies for fine-tuning models. Future studies in this domain should focus on developing optimized, lightweight, and domain-specific solutions that align practical needs with technical constraints [223].  
One of the most prominent manifestations of these technical complexities is the need for the optimal integration of multimodal inputs and their simultaneous processing within advanced architectural frameworks. With the expansion of LLMs, the integration of multimodal data including text, image, audio, and video has become one of the most transformative directions for future research [224]. In fields such as digital health [225], clinical data analysis, medical education, and virtual consultation, the ability of models to interpret and simultaneously process such diverse inputs can unlock a new level of intelligence and decision support. For example, in healthcare settings, combining a patient's textual medical history with medical images (e.g., MRI or CT scans) and even audio data (e.g., breathing sounds or patient conversations) can significantly enhance diagnostic accuracy and the quality of treatment recommendations.  
Recent developments such as Flamingo and GIT show that multimodal Transformer architectures can effectively model the interaction between natural language and visual inputs. Moreover, models like EmbodiedGPT, which leverage vision-language integration, are capable of being deployed in operational and clinical environments. However, challenges such as semantic alignment between heterogeneous data sources (text-image-audio), the scarcity of domain-specific labeled datasets in the medical field, low interpretability,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technical Focus** & & **Challenges** & **Challenge Solution** \\ \hline Heterogeneous data & & & \\ integration & \(\blacksquare\) & Risk of amplifying inequity & \(\blacksquare\) & Adversarial training \\ \hline Compute and & & & \\ resource efficiency & \(\blacksquare\) & Susceptibility to crafted, malicious inputs & \(\blacksquare\) & Input filtering and attack detection \\ \hline Long-term memory & & Aligning text, images, audio, & & \\ and reasoning & & and time series into one & & \\ coherent representation & & & \\ \hline Multimodal processing limits & & & \\  & & & \\  & & & \\  & & & \\ \hline Technical focus & & Limited context window difficulty & \(\blacksquare\) & Use ”late-fusion” ensemble methods \\  & & with multi-step logic & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._and high hardware requirements for the training and deployment of these models remain significant barriers. Future research should focus on designing lightweight architectures tailored to domain-specific data, developing multimodal fine-tuning methods [226], and building standardized and high-quality datasets for evaluating performance in real-world scenarios. Furthermore, enhancing model decision making transparency and developing interpretability tools for clinical professionals should be among the ethical and practical priorities in advancing this field.