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@@ -216,11 +216,11 @@ Compare with ChatGPT / Alpaca / Galactica
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 <span class="section-title"> Future Work </span>
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-One of the most crucial future works is the evaluation of NLP models within the biomedical field thoroughly. With the varying structure and type of medical data, it is essential to assess the efficacy of NLP models and their generalizability across different data sets. </br></br>
+One of the most crucial future works is the systematic evaluation of Visual Med-Alpaca, as well as other NLP models within the biomedical field. With the varying structure and type of medical data, it is essential to assess the efficacy of NLP models and their generalizability across different data sets. </br></br>
 
-Moreover, pretraining on medical data can enhance the performance of NLP models in the biomedical field. It can help in the identification of disease phenotypes and the representation of clinical concepts.</br></br>
+We also expect pretraining on medical data can enhance the performance of NLP models in the biomedical field. It should help in the identification and reasoning of disease phenotypes, drug mechanism and the representation of clinical concepts.</br></br>
 
-The addition of genome protein modality can help in achieving better reasoning in NLP models. Given that genetic and protein information are critical for understanding disease processes, NLP can aid in the analysis of large volumes of genomic data, making it possible to identify novel mutations involved in various disease processes. Therefore, incorporating genomic information into NLP models will enable a wider range of applications within the biomedical field.</br></br>
+The addition of genome protein modality may also help in achieving better reasoning in NLP models. Given that genetic and protein information are critical for understanding disease processes, NLP can aid in the analysis of large volumes of genomic data, making it possible to identify novel mutations involved in various disease processes. Therefore, incorporating genomic information into NLP models will enable a wider range of applications within the biomedical field.</br></br>
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