How can you build trust in AI for healthcare?

To establish trust in AI within healthcare, ensure the AI system is rigorously tested for reliability, safety, and accuracy. Be transparent about its capabilities and limitations. Involve healthcare professionals and patients in its development and prioritize privacy and security, building confidence in its application.

Transparency and stakeholder engagement are paramount to building trust in AI for healthcare. Start by clearly defining the problem, then continuously involve healthcare providers, patients, and regulators in development and decision-making processes. Employ responsible AI principles focusing on transparency, fairness, and accountability to align the system with ethical norms and business goals. Mitigate bias and risk by utilizing diverse data sets and conducting rigorous validation tests. Regular audits and performance monitoring help ensure ethical and legal compliance, fostering trust among all stakeholders. Faith is not just built but maintained through these vigilant, ongoing efforts.

In my experience, it all starts with “what problem are we trying to solve”, “to whom”, “is that problem worth solving” and “does it really need AI”. In healthcare, the problems are tied to 1. The one, who provides care to the patients. Eg: Caregivers, clinical specialists etc 2. The one, who is the recipient of the care. Eg: Patients, individuals. 3. The system, who orchestrates the care. Eg: healthcare provider, payer, Solution provider. Healthcare problems are complex and long standing, and with AI, the goal should be to reduce the complexity and not add to it.

In my experience, the main issues to address and stakeholders to talk to are as follows: Showing how AI can help solve everyday difficulties. For example, I expose to the radiologists that AI is a valuable support in performing/referring examinations, but it will never replace their expertise. To the clinic administration, I explain that AI can increase the volume of examinations and reduce the time for performing and reporting examinations. To the IT department, on the other hand, I explain how the sensitive data of patients are protected.

Firstly, strong ethical principles and regulations must be established to protect patient autonomy, safety, privacy, and equity. The World Health Organization outlines key ethical principles including transparency, responsibility, inclusiveness, and sustainability. Laws and guidelines will reassure stakeholders that ethical risks are being addressed. Involving stakeholders like patients, doctors and regulators throughout development and deployment ensures AI systems address real needs and concerns. Their insights build more trustworthy AI. Rigorous governance and oversight by experts further safeguards against negative impacts.

If there is one domain where AI cannot go wrong, that is healthcare. The system needs to be reliable, unfortunately nothing short of 100% is acceptable, achieving this would take proper definition of AI models combined with rigorous testing. What I have seen in my experience is that the models tend to behave differently during edge case scenarios. Having an extensive support from the healthcare experts is the best way to define the scenarios and its outcome, combined with an accurate, empathetic approach of analysing the results.

Building trust in AI for healthcare is like a doctor-patient relationship—it demands consistency, transparency, and a deep understanding. Just having vast data isn't enough. It's about the granularity, timeliness, and authenticity of that data. A minor bias can result in misdiagnosis or treatment errors. Additionally, AI isn't a one-size-fits-all; the methodology must resonate with the unique health nuances of individual patients. Periodic validation and adaptation of algorithms, based on real-world patient feedback and outcomes, can be pivotal. Ultimately, the blend of precise data and tailored algorithms can pave the way for a new era of trusted healthcare AI.

If patients have been interacting with AI chatbots or if AI was used to diagnose patients illnesses or to recommend personalised medecine, it is very important to make the patients aware of that. Not disclosing that AI has been used is unethical in my view.

Building trust in AI for healthcare involves involving and empowering stakeholders throughout the development and deployment process. This means engaging with users, patients, and regulators, and seeking their feedback and input. It also involves respecting their rights and preferences, providing them with options and controls over AI systems, and educating them on how to use and interact with these systems effectively. By actively involving and empowering stakeholders, you can create a collaborative and transparent environment that fosters trust in AI for healthcare.