AI in healthcare: Transforming today, shaping tomorrow (YEL 2025)

Authors: Khawla Alhajaj (UAE), Thomas Dejgaard (Norway), Jhey-Ar Mangati (Philippines) and Angela Zapp (Germany)

Reviewers: Samar Almuntaser (YEL 2020) and Olivier Costa (YEL 2022)

Artificial intelligence is transforming healthcare from reactive medical practice toward predictive, personalized, and accessible care. As members of the Young Executive Leaders (YEL) programme of the International Hospital Federation, we recognize that AI adoption is not merely a technical challenge but a leadership imperative that requires building trust, bridging inequities, and guiding teams through change.

The global context

Approximately 4.5 billion people worldwide lack access to essential health services, while a critical shortage exceeding 10 million health professionals is projected by 2030 (OECD, 2023). AI offers potential solutions by extending clinical expertise to underserved areas, improving diagnostic accuracy, optimizing resource allocation, and enabling predictive interventions.

The future by 2035

AI will reshape healthcare through four key transformations. As a workforce multiplier, AI will handle routine diagnostic tasks, reducing workload in mammography screening, stroke detection, and fracture identification while allowing clinicians to focus on complex decision-making (Lång et al., 2023; Nagendran et al., 2020; Husarek et al., 2024). Through precision medicine and continuous monitoring, AI will combine genetic, clinical, and digital signals to predict serious events like sepsis and cardiac arrest hours before they occur, enabling preventive action (Bhargava et al., 2024; Kim et al., 2019). Operational excellence will be achieved as AI handles administrative tasks that currently consume 25% of healthcare spending, reducing clinician burnout and improving efficiency (Chernew & Mintz, 2021; KPMG, 2025). Finally, digital twins will enable better planning by allowing healthcare systems to test interventions virtually before real-world implementation (Katsoulakis et al., 2024; Shen et al., 2024).

Key challenges

Three major barriers impede AI adoption. Trust and transparency remain difficult to build when AI systems function as “black boxes” that cannot explain their decision[1]making processes (Quinn et al., 2020; Khan et al., 2024). Data quality and interoperability challenges arise from fragmented, inconsistent healthcare data scattered across multiple systems (Nagendran et al., 2020; Isgut et al., 2023). Ethics and regulation require balancing innovation with patient privacy, fairness, and safety across different regulatory approaches (Gerke et al., 2020; Shoghli et al., 2024).

Global lessons

Case studies from four countries offer valuable insights. The Philippines demonstrates how resource constraints drive locally-tailored innovation through telemedicine and homegrown AI solutions. Norway shows that deliberate, safety-first approaches build the trust necessary for sustainable adoption through structured national planning(Helsedirektoratet,202). Germany illustrates the power of specialization, particularly in mental health and cancer detection, combined with regulatory pathways. The UAE exemplifies how comprehensive governance frameworks and strategic focus on precision medicine can accelerate progress while maintaining accountability, achieving measurable efficiency gains and positioning for world leadership in rare disease genomics (Department of Health, Abu Dhabi, 2018; Al Braiki et al., 2025; AlSerkal et al., 2025; Daw Elbait et al., 2021; Bizzari et al., 2023).

Leadership imperatives

Successful AI integration requires three core leadership commitments. Leaders must champion responsible AI by establishing clear governance frameworks, ethics committees, and accountability mechanisms that balance innovation with patient protection. They must invest in building capacity and upskilling everyone, creating cultures of continuous learning where all staff understand AI’s potential and limitations. Finally, leaders must guide the journey like ship captains, steering through change while empowering people and maintaining focus on improving patient outcomes and advancing health equity.

The path forward

The journey of integrating AI into healthcare is collective, requiring continuous learning and knowledge exchange. In an already strained healthcare system, the challenge is not a lack of tasks but rather a shortage of personnel. AI can relieve pressure by taking over routine or time-consuming work, yet this will also require employees to acquire new skills. As demonstrated through the YEL program, we must build platforms for sharing successes and failures across borders. Cross-border knowledge exchange is essential to ensuring that AI benefits all populations, not just those in well-resourced settings. The future of healthcare is not predetermined, it is something we are creating together through responsible leadership grounded in ethics, equity, and collaboration. Our commitment must be to ensure that AI becomes a force for good in healthcare, extending quality care to all who need it, regardless of where they live or their circumstances, ultimately achieving health for all.

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