Responsible Implementation of AI in Healthcare: Principles to Consider
Artificial intelligence (AI) technologies such as ChatGPT are increasingly being implemented across many industries including the healthcare sector.
Among the professional fields incorporating AI, medicine stands out as a field that can stand to gain substantial improvements. AI has already been successfully demonstrated in applications such as dictating medical records and interpreting radiographs. While these AI-driven tools offer such opportunities as improving diagnostic accuracy, predicting disease progression and developing personalized treatment, the use of AI in medicine raises ethical concerns around equity, accountability and privacy.
In order for the healthcare system to fully realize the benefits of integrated AI while mitigating these concerns, I believe implementation should follow a comprehensive framework encompassing these principles.
RESPONSIBLE IMPLEMENTATION
Patient Safety and Privacy
To continually prioritize the safety and privacy of patients, AI systems must be developed with rigorous testing to comply with up-to-date legal and industry standard guidelines. This minimizes the risk of patient privacy breaches and ensures that public trust can be maintained in these critical systems infrastructures. Similarly, government bodies should be involved with creating strict privacy regulations similar to the European General Data Protection Regulation (GDPR), a uniform data security law followed throughout European countries.
Interdisciplinary Collaboration
Everyone in the healthcare team should be heavily involved to ensure that the AI software is appropriately adopted into the clinical workflow. This not only includes professionals such as doctors and nurses, but also allied health professionals like social and hospice workers. Finally, the framework must be developed with input from patient representatives to ensure that we place patient outcomes at the forefront of AI’s implementation. Aside from the healthcare team, other stakeholders should be continually consulted. For example, regulatory officers can ensure that the algorithms comply with data policies and government bodies can establish proper checks and balances with relevant public health mandates. Only through continued interdisciplinary collaboration can we align the needs and expectations of all parties and responsibly implement AI to the healthcare sector.
Continuous Adaptation
Healthcare is an ever-evolving field. We are constantly gaining new information to help provide the best possible care for patients and AI should be able to adapt as well. AI trained on older datasets and knowledge may not be able to provide the most up to date care and thus, responsible incorporation of AI needs to consider the latest research and standards. Constant monitoring of the accuracy and performance of AI algorithms against the latest standards of care would allow clinicians to make changes as needed and ensure that the AI systems are meeting our goals.
Transparency
Patients should have the right to know about their treatment, including both risks and benefits. Keeping the public informed on the state of AI, and how the system plans to address its shortcomings ensures that patients can trust the technology as it becomes increasingly employed in various aspects of clinical care. Given the critical nature of healthcare as an industry, public trust in the system is integral to the effective delivery of care. Transparency in how these AI systems are trained and implemented would also allow for greater oversight and independent checks and balances to ensure that errors and biases are caught prior to any risks to patients' safety.
