Version 1
: Received: 31 July 2024 / Approved: 1 August 2024 / Online: 2 August 2024 (08:30:23 CEST)
How to cite:
Sobhani, N.; D'Angelo, A.; Pittacolo, M.; Mondani, G.; Generali, D. Future AI Will Be Able to Predict Antibody-Drug Conjugate Response in Oncology. Preprints2024, 2024080099. https://doi.org/10.20944/preprints202408.0099.v1
Sobhani, N.; D'Angelo, A.; Pittacolo, M.; Mondani, G.; Generali, D. Future AI Will Be Able to Predict Antibody-Drug Conjugate Response in Oncology. Preprints 2024, 2024080099. https://doi.org/10.20944/preprints202408.0099.v1
Sobhani, N.; D'Angelo, A.; Pittacolo, M.; Mondani, G.; Generali, D. Future AI Will Be Able to Predict Antibody-Drug Conjugate Response in Oncology. Preprints2024, 2024080099. https://doi.org/10.20944/preprints202408.0099.v1
APA Style
Sobhani, N., D'Angelo, A., Pittacolo, M., Mondani, G., & Generali, D. (2024). Future AI Will Be Able to Predict Antibody-Drug Conjugate Response in Oncology. Preprints. https://doi.org/10.20944/preprints202408.0099.v1
Chicago/Turabian Style
Sobhani, N., Giuseppina Mondani and Daniele Generali. 2024 "Future AI Will Be Able to Predict Antibody-Drug Conjugate Response in Oncology" Preprints. https://doi.org/10.20944/preprints202408.0099.v1
Abstract
The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize findings of recent late-phase clinical trials using these conjugates for cancer therapy.
Keywords
artificial intelligence; antibody drug conjugates; prognostic; clinical trials
Subject
Medicine and Pharmacology, Medicine and Pharmacology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
