One of the major challenges of machine learning in healthcare is ensuring algorithmic fairness and avoiding discrimination or unfair treatment of certain groups. When machine learning models are trained on health data, there is a risk that historical biases in that data could be learned and reinforced by the models. For example, if a model is trained on data where certain ethnic groups received less medical attention or worse outcomes, the model may learn biases against recommending treatments or resources to those groups. This could negatively impact health equity. Considerable research is focused on how to develop machine learning techniques that are aware of biases in data and can help promote fairness.

Another significant challenge is guaranteeing privacy and secure use of sensitive health data. Machine learning models require large amounts of patient data to train, but health information is understandably private and protected by law. There are risks of re-identification of individuals from their data or of data being leaked or stolen. Advanced technical solutions are being developed for privacy-preserving computing that allows analysis on encrypted data without decrypting it first. Complete privacy is extremely difficult with machine learning, and privacy risks must be carefully managed.

Generalizability is also a challenge, as models trained on one institution or region’s data may not perform as well in other contexts with different patient populations or healthcare systems. More data from diverse settings needs to be incorporated into models to ensure they are robust and benefit broader populations. Related issues involve the interpretability of complex machine learning models – it can be difficult to understand why certain predictions are made, leading to distrust. Simpler and more interpretable models may need to be developed for high-risk clinical applications.

Regulatory approval for use of machine learning in healthcare applications is still evolving. Clear pathways and standards have not been established in many jurisdictions for assessing safety and effectiveness. Models must be validated rigorously on new data to demonstrate they perform as intended before being deployed clinically. Post-market surveillance will also be needed as external conditions change. Close collaboration is required between technology developers and regulators to facilitate innovative, safe applications of these new techniques.

Informed consent for use of personal health data raises ethical questions considering the complexity and opacity of machine learning models. Patients and healthcare providers must understand how data will be used and the potential benefits, but also limitations and uncertainties. Transparency around data use, security safeguards, how individuals may access, change or remove their data, and consequences of opting out must be provided. The implications of consent may be challenging to comprehend fully, requiring support and alternatives for those who do not wish to participate.

Conflicts of interest and potential for commercial exploitation of health data also need oversight. While private sector investment is accelerating progress, commercialization could potentially undermine public health goals if not carefully managed. For example, companies may seek healthcare patents on discoveries enabled by the use of patient data in ways that limit access or increase costs. Clear benefit- and data-sharing agreements will be required between technology developers, healthcare providers and patients.

The appropriate roles and responsibilities of machines and humans in clinical decision making raise challenges. Some argue machines should only act as decision support tools, while others foresee greater autonomy as abilities increase. Complete removal of human clinicians could undermine the caring and empathetic aspects of healthcare. Developing machine learning solutions that best augment rather than replace human judgement and maintain trust in the system will be vital but complex to achieve. Substantial effort is required across technical, regulatory and social dimensions to address these challenges and realize the promise of machine learning in healthcare ethically and equitably for all. With open collaboration between diverse stakeholders, many believe the challenges can be overcome.