Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities

Challenges	Approach
Autonomy	Informed consent of the participating subjects should be the cornerstone of data collection, storage, and usage. Enhanced risk of confidentiality breaches should be further emphasized and mitigated.
Public engagement at all stages	A diversity of experts and all potential end-users should be actively consulted in the development and implementation of these new analytical tools. Given the open-ended nature of BD, impact assessment should be an ongoing and adaptive process.
Equity and managing biases	The AI-based analysis should give adequate weight to all the relevant populations and socio-environmental determinants of immunoregulation to avoid biases and potential injustice.
Protect vulnerable populations	The new analytics should strive to create equitable opportunities by researching illnesses that especially affect vulnerable populations and develop treatments that are well-tailored to their means and values. At the same time, these efforts must not add a greater burden on vulnerable populations.
Reliability and trust	The AI-generated models and treatments are based on partially opaque processes and offer a limited understanding of the mechanisms involved. Unless high standards in research and care are maintained, this has the potential to hinder reliability and trust toward experts and institutions using AI-assisted analyses and decision-making.

Strategy	Recommendation to respond
Streamline BD repositories	Develop guidelines that will streamline current and future immunological data repositories to help workflow and transparency.
Establish decision-making strategies for the use of AI-based big omics data analysis	Develop guidelines for decision-making as to how the analysis of “omics” data by AI/BD will use biomarkers for immunotherapy and regulation of immunity.
Advanced patient-centric “Precision Medicine” approaches	Develop approaches that are primarily patient-centric and do not depend uniquely on aggregated data from BD sets for AI analysis.
Develop strategies to address unforeseen adverse effects	Develop guidelines as to what steps and alternatives should be considered if AI-guided analysis predicts undocumented side effects or fails to predict side effects.
Incorporate the role of microbiota-immune cell interaction in immunoregulation	Develop complementary tracks to analyze BD involving interaction between innate and adaptive immune cells with microbiota.
Develop strategies to use AI/BD analysis to address knowledge gaps in the regulation of immunity and advancing immunotherapy	Develop strategies to use the analysis of omics and other immunological datasets by AI/BD tools to understand molecular and cellular mechanisms to address knowledge gaps for regulating immunity and immunotherapy in health and disease states.

Częstotliwość wydawania:: Volume Open
Dziedziny czasopisma:: Medicine, Basic Medical Science, Biochemistry, Immunology, Clinical Medicine, other, Clinical Chemistry