Table 2 Comparison based on different parameters

Bias

Race and ethnicity bias

Bias in AI-based medical imaging for light-skinned individuals.

Gender bias

Health disparities for women in ethnic minority groups.

Geographical disparities

Amplification of bias in retrospective studies.

Clinical trial bias

Minimal representation of certain populations, which raises efficacy concerns.

Socioeconomic bias

The undervaluation of healthcare costs for certain demographic groups, which affects algorithms.

Algorithmic bias in various applications

Biases in algorithms used to determine kidney function and perform facial recognition.

Federated learning as a solution

Potential accessibility issues for small institutions and corporate dominance.

Regulations and policy

International norms

Recommendations from the WHO, the USA’s AI Bill of Rights, and the European Commission.

Fairness and health inequities

Need for strong regulatory standards and guidelines to address potential health inequities.

Dedication to diversity

Legislative protections for the use of AI to address rare diseases in line with the UN’s Sustainable Development Goals.

Uniform legal frameworks

A lack of such frameworks, which entails compliance challenges, thus highlighting the necessity of state supervision.

Privacy

Challenges pertaining to data transfer

The simplification of data transfer through digitalization, which nevertheless introduces challenges related to security and auditing.

Ethical principles

Inadequate exploration of the influence of ethical principles on AI models.

Need for AI regulations

The necessity of AI regulation in healthcare, especially with regard to unintended causal patterns.

Inclusion

Balanced datasets

Essential for model quality and the avoidance of errors.

Community engagement

Essential for avoiding biases; inclusivity is a moral and strategic imperative.

Patient-centric AI

The need for AI to incorporate gender, sex, and socioeconomic factors comprehensively.

Equity

Dual effects of AI

The fact that AI may either promote or impede health equity.

Addressingvulnerable populations

An emphasis on the needs of vulnerable populations through equitable data management and testing methodologies.

NLP in patient-centric care

Identification of NLP as a powerful tool for patient-centric care, which can promote equity.

Validation

Challenges pertaining to clinical validation

Challenges that highlight the need for real-world evidence and comprehensive testing methodologies.

Importance of empirical evidence

A regulatory emphasis on empirical evidence to support the safety, efficacy, and equity of the use of AI in healthcare.

Nuanced model performance

The necessity of validation in diverse domains.

Global impact

Regional disparities in adoption

The fact that developed countries exhibit advanced integration, thus leading to variations in healthcare outcomes.

Variations in outcomes and efficacy

Geographical disparities, which result in varying outcomes and context-dependent effectiveness.

Ethical considerations

Essential for inclusive AI deployment.

Public perceptions

Awareness and education

The positive influence of increased awareness on perceptions, especially among individuals with diverse backgrounds.

Trust-building measures

Transparent communication and community engagement, which contribute to the establishment of trust.

Cultural sensitivity in AI design

A positive influence on public trust by respecting diverse norms and values.

Community engagement

Community engagement in decision-making processes, which establishes trust.

Ethical considerations and accountability

Public trust, which is influenced by ethical frameworks and clear accountability measures in the context of AI applications.

Addressing bias and fairness

Efforts to enhance fairness and equity, which resonate positively with diverse populations.

Intersectionality in trust dynamics

The recognition of intersectionality in trust dynamics, including the fact that trust is influenced by various factors such as race, gender, and socioeconomic status.