  Foundational Models (FMs) are emerging as the cornerstone of the biomedical
AI ecosystem due to their ability to represent and contextualize multimodal
biomedical data. These capabilities allow FMs to be adapted for various tasks,
including biomedical reasoning, hypothesis generation, and clinical
decision-making. This review paper examines the foundational components of an
ethical and trustworthy AI (ETAI) biomedical ecosystem centered on FMs,
highlighting key challenges and solutions. The ETAI biomedical ecosystem is
defined by seven key components which collectively integrate FMs into clinical
settings: Data Lifecycle Management, Data Processing, Model Development, Model
Evaluation, Clinical Translation, AI Governance and Regulation, and Stakeholder
Engagement. While the potential of biomedical AI is immense, it requires
heightened ethical vigilance and responsibility. For instance, biases can arise
from data, algorithms, and user interactions, necessitating techniques to
assess and mitigate bias prior to, during, and after model development.
Moreover, interpretability, explainability, and accountability are key to
ensuring the trustworthiness of AI systems, while workflow transparency in
training, testing, and evaluation is crucial for reproducibility. Safeguarding
patient privacy and security involves addressing challenges in data access,
cloud data privacy, patient re-identification, membership inference attacks,
and data memorization. Additionally, AI governance and regulation are essential
for ethical AI use in biomedicine, guided by global standards. Furthermore,
stakeholder engagement is essential at every stage of the AI pipeline and
lifecycle for clinical translation. By adhering to these principles, we can
harness the transformative potential of AI and develop an ETAI ecosystem.
