Novel ethical and explainable artificial intelligence based digital medicines and treatments

We have described validation of novel machine learning architectures for designing faster, safer, and more efficacious digital medicines in our published research findings [1]. This work has significant impact on the ethical decisions facing patients and their families, and regulatory decisions for the United States Food and Drug Administration (FDA) and European Medical Agencies (EMA) [2]. For example: Phase 3 clinical outcome trials evaluating new therapies, and vaccines are among the most complex experiments performed in medicine. Around 50% of Phase 3 trials fail [3]. The US FDA states that a common theme is the difficulty of predicting clinical results in a wide patient base. More importantly, the barriers to this cost healthcare industries, government, and academic research hospitals millions of dollars each year, as well as drive up costs, delay life-saving treatments to patients, and in some cases lead to adverse events . We invent ethical and explainable AI and machine learning systems which learn from diverse and inclusive datasets. Our research classifies, predicts and enriches novel digital endpoints to benefit patient health, eliminate adverse events, and improve outcomes while managing diseases and pioneers a regulatory path for AI and ML in medical care [4].


  1. Reinforcement learning with action-derived rewards for chemotherapy and clinical trial dosing regimen selection. 2018. [Abstract] [Full Paper]

    Gregory Yauney, Shah P*

    (*Senior author supervising research)

    Proceedings of the 3rd Machine Learning for Healthcare Conference. PMLR 85:161-226

  2. Artificial intelligence and machine learning in clinical development: a translational perspective. 2019. [Abstract] [Full Paper]

    Pratik Shah* et al.

    (*Corresponding author)

    Nature Digital Medicine. DOI: 10.1038/s41746-019-0148-3

  3. Artificial Intelligence for clinical trial design. 2019. [Abstract] [Full Paper]

    Stefan Harrer, Shah P, Antony P, Hu J

    Trends in Pharmacological Sciences. PMID: 31326235

  4. Improving cancer diagnosis and care: clinical application of computational methods in precision oncology. 2019. [Abstract] [Full Paper]

    Pratik Shah

    Proceedings of a Workshop, National Academies of Sciences, Engineering, and Medicine. Washington, DC: The National Academies Press.

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