use cases

Educational Assistant for Clinical Protocol Management

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


Clinical and medical literature and clinical protocols are present in the health environment and constantly evolving, but it is saved in countless publications in which doctors cannot have access in real time and in an agile way to react to the current situation of the patient and generate adherence to a treatment.

To make medical decisions, health professionals follow patient care processes that are based on the following parameters: availability of objective and reliable evidence, research, interpretation of existing facts and weighing of risks and benefits (1). However, on certain occasions, this standardized protocol does not work because the evidence is not available in real time, time is limited, or because decisions about certain patients cannot always be objective (1).The treatment of diseases in primary care is challenging due to symptom variability and new, unknown existing variants, leading to difficulties in early and accurate diagnosis.

Size of the Problem:

  • On average, doctors spend about 8 hours a week searching for medical information. (2)
  • According to a study conducted by Stanford University, the use of AI-based support tools can reduce diagnostic errors by 30% and improve diagnostic accuracy by 20%.
  •  Time is a limited resource in the clinical setting as manually searching for relevant information can be time-consuming and impractical.
  •  Up to 50% of the information consulted by doctors is not used in clinical decision making, which represents an exhaustion of time and resources (6).
  • Quick access to relevant medical information can reduce medical costs by up to 25% by avoiding unnecessary tests and ineffective treatments (6).

Why it matters

  • The learning methods of health professionals need to be improved because the information has become more complex or requires some type of training (3).
  • Artificial intelligence can create different scenarios that offer numerous advantages from a training point of view: simulation of real interventions, training of diagnostic or communication skills with the patient and the creation of medical assumptions based on real parameters (3).
  • The generation of clinical protocols, which guides decisions and criteria regarding diagnosis, management and treatment in specific areas of health, needs adequate optimization in development time since it can delay the progress of research and limit access to the information (4).
  •  Fuzzy logic -used in new research models- is reporting new visions in the field of health that improve the study of diseases, diagnostic systems and of treatment responses (3).


  1. AI educational and management tools: AI assistants provide educational, management and support tools to help professionals in their medical decisions. They reduce timelines and minimise risks by collecting, analysing and processing data. For example, DeepHealth: A European project that improves medical diagnostics with AI support tools, enabling doctors to diagnose, monitor and treat patients more efficiently (5).
  2. Real-time conversational AI models: Conversational AI models based on medical literature and clinical protocols allow healthcare professionals to dialogue in real time with an assistant. This interaction improves early detection and identification of the most appropriate treatment protocols. For example,  MedSearch Pro: An advanced AI assistant that leverages PubMed data to provide fast, accurate answers and summaries. MedSearch Pro provides personalised recommendations and helps professionals access clinical protocols to make informed patient care decisions.
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  • Research papers.
  • Clinical guides.
  • Medical Literature.
  • Peer review journals.


(1) N. Joison, R.J. Barcudi, E.A. Majul, S.A. Ruffino, J.J. De Mateo Rey. Artificial intelligence in medical education and health prediction. Method Magazine. Catholic University of Córdoba [Internet]. January 2021.

(2) World Health Organization, WHO (s/f). Tests for action. Abril de 2024.

(3) F. Avila-Tomás, M.A. Mayer-Pujadas, V.J. Quesada-Varela. Artificial intelligence and its applications in medicine II: Current importance and practical applications. Elsevier [Internet]. January 2021.

(4) Institute of Medicine (US) Forum on Drug Discovery, Development, and Translation. Transforming Clinical Research in the United States: Challenges and Opportunities: Workshop Summary. Washington (DC): National Academies Press (US); 2010. 2. Miller, JE. Institutional Review Board: Management and Function. Jones and Bartlett Publishers; 2005.

(5) I. Medinaceli Díaz, M.M. Silva Choque. Impact and regulation of Artificial Intelligence in the healthcare field. Scielo [internet]. March 2022. 

(6) Núñez, Diego, Mascaró, Jordi, Quecedo, Luis, Gol-Montserrat, Jordi, del Llano Señarís, Juan. Artificial Intelligence and Clinical Decisions: How doctor behavior is changing.Researchgate. December 2020.

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