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Tovar et al. Art Int Surg 2023;3:14-26 https://dx.doi.org/10.20517/ais.2022.38 Page 22

Figure 2. Summary of the development and implementation of AI in medicine [67,73,75,77] .

The start of every model building begins with thinking of its purpose and reviewing literature on the
appropriate material and current models for the development of a clinically useful model. An appropriate
AI algorithm is chosen with consideration of its desired purpose and the maintenance of patient privacy and
consent. External evaluation will provide the most accurate analysis of the model's reproducibility, which is
[75]
important for further clinical trials .

With the growing complexity of AI used and its influence in medicine, there is a need to provide
transparent reporting in its trials. In a study evaluating image-based diagnostic AI study design, only 6% of
papers examined included external validation in their methodology, an essential component for thorough
clinical evaluation . The minimum information about clinical artificial intelligence modeling (MI-CLAIM)
[76]
checklist was intended to provide transparency in the documentation of the development of these
algorithms, including an evaluation of bias and instructions for external reproducibility. In each clinical
trial, MI-CLAIM starts with describing the study design, where the researcher answers: 1. What will the
algorithm be answering, and how would this fit in a real-world scenario? 2. How is the performance
measured and how is it used to evaluate its performance in a clinical setting? 3. Is the cohort representative
of a real-world population? 4. Is the testing model performing better than the current models?

Next, the MI-CLAIM has the researcher document each step in the model testing and training, highlighting
the methods by which groups were separated to ensure the testing model is representative of the clinical
population. The model’s type is then selected, describing which were the best parameters found and how the
data was picked, cleaned, and formatted. Statistical performance will be listed, as well as clinical
performance evaluators, such as specificity and sensitivity. An examination of the model will provide
readers and evaluators with information on the model’s performance, reliability, and significance in the
field. To implement the AI in the clinical setting, the researcher's ultimate goal, the code, computer
requirements, notes, or any factors needed for the model building are provided or externally evaluated for
reproducibility and accuracy .
[77]

After conducting the clinical study, each model must receive approval from the governing health institution
for its clinical adoption. The Food and Drug Administration (FDA) is the governing institution in the
United States regulating the clinical implementation of medical technology and treatments. Furthermore,
AI models utilized in hospitals need to be monitored and regulated in their practice, considering the ethical
and privacy concerns involved, including the requirement of patient consent. Physicians can consider its
[75]
use and how much influence the AI will have in decision-making [Figure 2] .

CONCLUSION
This review summarizes the recent developments in which AI has the potential to aid early detection efforts.
Risk prediction models have been developed by focusing on factors associated with PDAC, such as new-