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Page 21 Tovar et al. Art Int Surg 2023;3:14-26 https://dx.doi.org/10.20517/ais.2022.38

biomarker. With the addition of CA 19-9 serum marker to the model, accuracy in predicting PDAC
improved, performing with an AUC of 0.94. Future development of AI and microbiome populations may
provide an accessible and noninvasive population-wide method of detecting PDAC during a curable
[52]
stage .

FUTURE DIRECTIONS AND CLINICAL ADOPTION
Federated learning
Research collaborations between different institutes can provide more meaningful data for model training,
especially when studying rare diseases such as PDAC. The federated learning approach to collaboration
involves sending computer models from one institution to another without sending or exchanging patient
[67]
data . In the standard development of models, concerns over patient privacy remain a large barrier to the
collaboration and expansion of data sets. Federated learning is beneficial in that the patient's information
stays locally within the institution . The use of federated learning in pancreatic cancer early detection
[68]
remains in a nascent phase. As PDAC is a heterogeneous and relatively rare cancer, utilization of more data
that spans institutions and demographics is expected to strengthen the ability of AI to predict the risk of
malignancy or detect early, potentially curable stages of disease with wider applicability. Indeed, bias is a
significant challenge to overcome with AI model building efforts, including the inclusion of
underrepresented minorities, rare conditions, and disadvantaged socioeconomic groups. Some examples of
successful federated learning in medical literature include its use in predicting future hospitalizations of
patients with heart diseases using EHR and in COVID-19 diagnosis using X-Ray and ultrasound
images [69,70] . Ongoing efforts through NIH will apply this form of collaboration to PDAC early detection .
[71]
Beyond risk stratification: subtyping PDAC biology for personalized screening
Several elements may be implemented in future AI model building to ensure optimum performance,
accuracy, and personalization. PDAC is a heterogeneous disease where treatment response, tumor growth
rate, and clinical outcomes vary. Thus, having a customized screening plan for each patient would make
detection at an early stage more likely. In aggressive subtypes, such as high delta tumors , doubling time of
[72]
tumor growth was observed to be faster than those of the less aggressive low delta subtype. Moreover, in
comparison to the patients with low delta tumors at diagnosis, the patients with high delta tumors at
diagnosis were associated with higher blood glucose levels in the pre-diagnostic period, faster wasting of
muscle and fat, and more advanced, incurable stages at diagnosis. Creating an AI model that predicts
whether a patient will have an aggressive or indolent form of the cancer may help form scheduled
surveillance better suited to detect signs of malignancy before metastasis .
[53]
Clinical application of AI models
Multiple challenges remain with clinical implementation of AI for early detection of PDAC. Awareness of
the ethical and privacy concerns involved in examining patient data at population scales is essential to
creating a trustworthy model. Privacy underprotection and overprotection of patient information is a major
concern when using big data. While underprotecting data can lead to breaches in privacy, overprotecting
can inhibit or block innovation . In the context of PDAC, new developments that balance data protection
[73]
concerns are needed as early detection strategies are integrated into health systems. In addition, there are
ethical pitfalls in implementing AI models in a healthcare setting. For example, there may be instances when
the AI and physician disagree on a diagnosis, where the physician can explain their reasoning in their
judgment, whereas AI cannot provide an explanation. Without a clear justification, the patient may not be
given enough information to make the best decision for his or her own health. The physician may keep their
original diagnosis, but in the case that it is wrong, it will appear as if they were disregarding crucial
evidence. They may also be pressured into agreeing with the model, trusting its accuracy more than their
[74]
clinical judgement .