Global Health Outlook 2025: Artificial Intelligence Set to Revolutionize Worldwide Healthcare, Encouraging Decision-Makers to Take Immediate Action
In the rapidly evolving landscape of healthcare, Artificial Intelligence (AI) is poised to revolutionize patient care. However, trust gaps between clinicians and patients can significantly undermine its adoption and effectiveness.
A recent study reveals that over 75% of clinicians are uncertain about liability for AI-driven errors, and 34% more patients are skeptical about AI compared to clinicians, with skepticism especially high among patients aged 45 and older [1]. The perceived "black-box" nature of AI algorithms, lack of adequate training, workflow disruption, ethical concerns, potential biases, and unresolved liability questions are key trust issues that need to be addressed [1][4].
To earn trust and deliver real impact in patient care, AI must be designed with people at the centre, built in collaboration with clinicians, focused on safety, fairness, and representation. Strategies suggested to build trust and accelerate AI adoption include transparency and explainability, human-centred design and collaboration, training and education, ethical safeguards and fairness, demonstrating tangible benefits, and iterative development and feedback [1][2][3][4].
Transparency and explainability aim to reduce the "black-box" effect by providing interpretable outputs that explain what data influenced predictions and why specific recommendations were made, aligning with clinicians’ evidence-based decision processes [1][3]. Human-centred design and collaboration involve engaging clinicians, nurses, administrators, and patients early and continuously in the design and deployment of AI, ensuring solutions address real needs without adding burdens and demonstrate clear value to clinical workflows and patient outcomes [2].
Improving AI literacy among healthcare professionals enhances their confidence and competence in using AI tools effectively, addressing concerns about inadequate understanding and usage [1][4]. Explicitly addressing concerns about bias, fairness, and ethical risks is crucial to maintain stakeholder trust and ensure equitable AI application [1][4]. Showing clear utility, such as reducing documentation time, automating routine tasks, or improving clinical accuracy, helps build clinician trust and encourages adoption [2]. Rapid prototyping, hands-on testing, and continuous user feedback promote trust by adapting AI systems to fit clinical realities and workflows [2].
The projected shortfall of 11 million health workers by 2030 could leave millions without timely care. AI could potentially double patient capacity as AI agents assist, learn, and adapt alongside clinicians by 2030, alleviating the burden of administrative tasks and allowing clinicians to focus on patient care [5]. However, AI must be implemented judiciously, addressing concerns about data inefficiencies. One-third of healthcare professionals lose over 45 minutes per shift due to data inefficiencies, amounting to 23 full days a year lost by each professional [6].
In conclusion, closing trust gaps requires transparent, human-centred AI design paired with education, ethical vigilance, and clear demonstration of clinical value to support acceptance and effective integration of AI into healthcare practice [1][2][3][4]. Royal Philips, a global health technology leader, has released its 10th annual Future Health Index (FHI) report, indicating AI holds promise for transforming care delivery [7]. The FHI 2025 Report highlights the need for continued collaboration between healthcare professionals, policymakers, and technology providers to ensure AI is developed and implemented in a way that benefits patients, clinicians, and the healthcare system as a whole.
References:
[1] "Trust gaps between clinicians and patients can significantly undermine the adoption and effectiveness of AI in healthcare" - Source
[2] "Strategies suggested to build trust and accelerate AI adoption include" - Source
[3] "AI tools should provide interpretable outputs explaining what data influenced predictions and why specific recommendations were made" - Source
[4] "Lack of adequate training, workflow disruption, ethical concerns" - Source
[5] "AI could automate administrative tasks, potentially doubling patient capacity as AI agents assist, learn, and adapt alongside clinicians by 2030" - Source
[6] "One-third of healthcare professionals lose over 45 minutes per shift due to data inefficiencies" - Source
[7] "Royal Philips, a global health technology leader, has released its 10th annual Future Health Index (FHI) report" - Source
Digital health technologies, such as AI, hold great promise for revolutionizing patient care and addressing the looming shortfall of 11 million health workers by 2030. However, trust gaps between clinicians and patients, stemming from concerns about liability, ethics, and workflow disruption, need to be addressed for AI to deliver its full potential in healthcare [1]. To build trust, AI must be transparent and explainable, human-centred, ethically sound, and demonstrably beneficial for both clinicians and patients [2][3]. By focusing on these key aspects, AI can not only assist but also learn and adapt alongside clinicians, potentially doubling patient capacity through the automation of administrative tasks [5]. The Future Health Index (FHI) 2025 Report stresses the importance of continued collaboration between healthcare professionals, policymakers, and technology providers to ensure that AI is developed in a way that benefits everyone involved in healthcare, ultimately improving health-and-wellness outcomes and medical-condition management [7].
