An overview of the use of cutting-edge artificial intelligence (AI) modeling to produce synthetic medical data (SMD) in decentralized clinical machine learning (ML) for ovarian cancer(OC) and ovarian lymphoma(OL).

AIM

o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease.

MATERIAL AND METHODS

ultilizing programmed detection of heterogeneus OL and OC habitats through radiomics and correlate to imaging based tumor grading plus a literature review.

RESULTS

new analysis pipelines have been generated for integrating imaging and patient demographic data and identify new multi-omic biomarkers of response prediction and tumour grading using cutting-edge artificial intelligence (AI) in OL and OC.

DESCRIPTION

deline the main AI methods used in OL and OC that we can try to standardize in the clinical radiological and medical practice to ameliorate the patients diagnosis and theraphy.

CONCLUSION

through new AI methods it's possible to combine research into a SwarmDeepSurv, generate new data flow channels, create medical imaging data channels of OL and OC using AI and identify new biomarkers of OL and OC. .