Healthcare organizations have an ethical obligation to ensure AI algorithms used for clinical decision making are transparent, interpretable, and free from biases that could negatively impact patients. There are several proactive steps organizations should take.

First, organizations must commit to algorithmic transparency as a core value and establish formal governance structures, such as oversight committees, to regularly audit algorithms for biases, errors, and other issues that could compromise care. Clinicians, data scientists, ethicists, and patients should be represented on these committees to bring diverse perspectives. Their role is evaluating algorithms throughout the entire development life cycle from design to deployment.

Next, algorithm design must prioritize interpretability and explainability from the outset. “Black box” algorithms that operate as closed systems are unacceptable in healthcare. Developers should opt for intrinsically interpretable models like decision trees over complex neural networks when possible. For complex models, techniques like model exploration tools, localized surrogate models, and example-based explanations must be incorporated to provide clinicians insights into how and why algorithms generated specific predictions or recommendations for individual patients.

During model training, healthcare organizations should ensure their data and modeling protocols avoid incorporating biases. For representative clinical algorithms, training data must be thoroughly evaluated for biases related to variables like age, gender, ethnicity, socioeconomic status and more that could disadvantage already at-risk patient groups. If biases are found, data balancing or preprocessing techniques may need to be applied, or alternative data sources sought to broaden representation. Modeling choices like selection of features and outcomes must also avoid encoding human biases.

Rigorous auditing for performance differences across demographic groups is essential before and after deployment. Regular statistical testing of model predictions for different patient subpopulations can flag performance disparities requiring algorithm adjustments or alternative usage depending on severity. For example, if an algorithm consistently under- or over- predicts risk for a given group, it may need retraining with additional data from that group or restricting use cases to avoid clinical harms.

Once deployed, healthcare AI must have mechanisms for feedback and refinement. Clinicians and patients impacted by algorithm recommendations should have channels to report concerns, issues or question specific outputs. These reports warrant investigation and may trigger algorithm retraining if warranted. Organizations must also establish processes for re-evaluating algorithms as new data and medical insights emerge over time to ensure continued performance and accommodation of new knowledge.

Accessible mechanisms for consent and transparency with patients are also required. When algorithms meaningfully impact care, patients have a right to easily understand the role of AI in their treatment and opportunities to opt-out of its use without penalty. Organizations should develop digital tools and documentation empowering patients to understand the limitations and specific uses of algorithms involved in their care in non-technical language.

Ensuring unbiased, transparent healthcare AI requires sustained multidisciplinary collaboration and a culture of accountability that prioritizes patients over profits or convenience. While complex, it is an achievable standard if organizations embed these strategies and values into their algorithm design, governance, and decision-making from the ground up. With diligence, AI has tremendous potential to augment clinicians and better serve all communities, but only if its development follows guidelines protecting against harms from biased or opaque algorithms that could undermine trust in medicine.

Through formal algorithmic governance, prioritizing interpretability and oversight from concept to clinical use, carefully addressing biases in data and models, continuous performance monitoring, feedback mechanisms, and consent practices that empower patients – healthcare organizations can establish the safeguards necessary to ensure AI algorithms are transparent, intelligible and developed/applied in an unbiased manner. Upholding these standards across the medical AI field will be paramount to justify society’s trust in technology increasingly playing a role in clinical decision making.