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MedSearch: Enhance Learning and Research Efficiency

AI offers educational support tools for doctors in their daily basics to efficiently manage patients.

Problem

Physicians face an overwhelming andconstantly growing volume of scientific information.

Annually, over 2.5 million articles are published in scientific journals, making it difficult for healthcareprofessionals to keep up with relevant advances [1].

Why it matters

  • 40% of physicians report difficulties inquickly accessing accurate, up-to-date medical information, directly impactingdiagnostic and treatment quality [2].
  • The cost associated with medical errors in the U.S. exceeds $20 billionannually, a problem exacerbated by poorly informed clinical decisions.
  • Physicians spend about 8 hours a week searchingfor information, and up to 50% of that information is often not used to makedecisions.

Solution

MedSearch is a search platform that reads medical  literature to resolve doubts and concerns in any clinical field based on updated scientific evidence.

Retrieval-augmented generation (RAG)  system to:

- Retrieve reliable information from  databases such as PubMed and official clinical guidelines.

- Answer questions in natural language in  real time, categorized into specific flows (clinical reference, research,  diagnoses, general questions).

- Implement LLMs in multiple languages ​​(English, Spanish and Portuguese), providing answers in the language chosen by the user.

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Datasources

Use reliable information sources, selectedand categorized according to the type of medical query. For questions relatedto general clinical references and diagnostic protocols, use the Google API tosearch for official clinical practice guidelines. For clinical researchquestions, use the PubMed API, transforming queries into PICO (Patient,Intervention, Comparison, Outcome) format to maximize the relevance of theresults. For general questions that require a broader search, also use Googleto summarize key findings.

Citations

  1. N, Joison; J, Barcudi; A, Majul; A, Ruffino; J, De; M, Joison;     & G, Baiardi. (2021). Artificial Intelligence in Medical     Education and Health Prediction. Methodo: Applied Research in     Biological Sciences, 6. https://doi.org/10.22529/me.2021.6(1)07
  2. Villa, M. C., Llano, I., Castaño-Villegas, N., et al.     (2024). Vitruvius: A Conversational Agent for Real-Time     Evidence-Based Medical Question-Answering. medRxiv. Retrieved     from https://doi.org/10.1101/2024.10.03.24314861
  3. Avila-Tomás, F., Mayer-Pujadas, M. A., & Quesada-Varela, V.     J. (2021). Artificial Intelligence and Its Applications in     Medicine II: Current Importance and Practical Applications. Elsevier     [Internet], January 2021.
  4. BVS (2020). Artificial Intelligence in Health. Retrieved     from https://pesquisa.bvsalud.org/portal/resource/pt/biblio-1379404
  5. Fundación Gaspar Casal. (2020). Artificial Intelligence     and Clinical Decisions. Retrieved from https://fundaciongasparcasal.org/inteligencia-artificial-y-decisiones-clinicas-como-esta-cambiando-el-compartimento-medico/

Problem

Physicians face an overwhelming andconstantly growing volume of scientific information.

Annually, over 2.5 million articles are published in scientific journals, making it difficult for healthcareprofessionals to keep up with relevant advances [1].

Problem Size

  • 40% of physicians report difficulties inquickly accessing accurate, up-to-date medical information, directly impactingdiagnostic and treatment quality [2].
  • The cost associated with medical errors in the U.S. exceeds $20 billionannually, a problem exacerbated by poorly informed clinical decisions.
  • Physicians spend about 8 hours a week searchingfor information, and up to 50% of that information is often not used to makedecisions.

Solution

MedSearch is a search platform that reads medical  literature to resolve doubts and concerns in any clinical field based on updated scientific evidence.

Retrieval-augmented generation (RAG)  system to:

- Retrieve reliable information from  databases such as PubMed and official clinical guidelines.

- Answer questions in natural language in  real time, categorized into specific flows (clinical reference, research,  diagnoses, general questions).

- Implement LLMs in multiple languages ​​(English, Spanish and Portuguese), providing answers in the language chosen by the user.

Opportunity Cost

Time Optimization: Retrieve evidence-based information 4.4 times faster.

Transparency: Admits when no information is found.

Accuracy: Achieves 90.26% accuracy in medical responses.


Impact

  • Enhances precision in medical responses (90.26%, exceeding leading models like GPT-4o) [3].
  • Reduces time required to access critical information, improving clinical decision-making.
  • Promotes continuous learning for physicians without replacing clinical judgment.
  • AI-driven clinical decisions can cut diagnostic time by 30% [5].


Data Sources

Use reliable information sources, selectedand categorized according to the type of medical query. For questions relatedto general clinical references and diagnostic protocols, use the Google API tosearch for official clinical practice guidelines. For clinical researchquestions, use the PubMed API, transforming queries into PICO (Patient,Intervention, Comparison, Outcome) format to maximize the relevance of theresults. For general questions that require a broader search, also use Googleto summarize key findings.


References

  1. N, Joison; J, Barcudi; A, Majul; A, Ruffino; J, De; M, Joison;     & G, Baiardi. (2021). Artificial Intelligence in Medical     Education and Health Prediction. Methodo: Applied Research in     Biological Sciences, 6. https://doi.org/10.22529/me.2021.6(1)07
  2. Villa, M. C., Llano, I., Castaño-Villegas, N., et al.     (2024). Vitruvius: A Conversational Agent for Real-Time     Evidence-Based Medical Question-Answering. medRxiv. Retrieved     from https://doi.org/10.1101/2024.10.03.24314861
  3. Avila-Tomás, F., Mayer-Pujadas, M. A., & Quesada-Varela, V.     J. (2021). Artificial Intelligence and Its Applications in     Medicine II: Current Importance and Practical Applications. Elsevier     [Internet], January 2021.
  4. BVS (2020). Artificial Intelligence in Health. Retrieved     from https://pesquisa.bvsalud.org/portal/resource/pt/biblio-1379404
  5. Fundación Gaspar Casal. (2020). Artificial Intelligence     and Clinical Decisions. Retrieved from https://fundaciongasparcasal.org/inteligencia-artificial-y-decisiones-clinicas-como-esta-cambiando-el-compartimento-medico/

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