Flaws (and quality) in research today: can artificial intelligence intervene?

The existing flaws in both conducting and reporting of research have been outlined and criticized in the past. Weak research design, poor methodology, lack of fresh ideas and poor reporting are the main points to blame. Issues have been continually raised on the types of results published, review process, sponsorship, notion, ethics, and incentives in publishing, the role of regulatory agencies and stakeholders, the role of funding, and the cooperation between funders and academic institutions and the training of both clinicians and methodologists or statisticians. As a result, there is loss of the utmost goal: the production of robust research to form recommendations to support pragmatic decision in a real-world context. We propose the construction of a model based on artificial intelligence that could assist stakeholders, clinicians, and patients to guide conducting the best quality of research. We briefly describe the levels of the workflow, including the input and output data collection, the feature extraction/selection, the architecture, and parameterization of the model, along with its training, operation, and refinement.