Promoting a culture of transparency, accuracy, and respect for patient data could be essential to unlocking the full potential of AI in healthcare, according to a healthcare data analyst.
The majority of healthcare professionals across the Asia-Pacific region now acknowledge the importance of adopting AI technologies to enhance care delivery, boost clinical and operational efficiency, and improve equitable access and health outcomes—particularly in the face of increasing demand and workforce shortages.
According to the latest Philips 2025 Future Health Index report, most surveyed professionals in the region believe that digital tools, including AI and predictive analytics, can help lower hospital admission rates and enable earlier interventions that save lives. Many are also actively engaged in developing and implementing these technologies within their organisations.
However, concerns around trust and effective implementation continue to persist. The Philips survey revealed that many healthcare professionals feel current technologies are not tailored to their specific needs. Additionally, there are worries about potential data biases in AI systems that could exacerbate disparities in health outcomes.
In a follow-up article published in the *Journal of Intelligent Learning Systems and Applications* by Scientific Research Publishing, Rohan Desai examined these challenges in greater depth and outlined a roadmap for advancing research and practical implementation of predictive analytics in healthcare.
The proposed roadmap emphasizes the use of hybrid machine learning models, such as stacking, boosting techniques, and combinations like neural network–random forest hybrids. These approaches harness the strengths of different algorithms: stacking can reduce bias and variance by combining multiple models, boosting iteratively improves performance, and hybrid models are capable of capturing complex nonlinear patterns while preserving a level of interpretability.
A recent study from the United States also explored key barriers to implementing predictive analytics in healthcare. According to business intelligence analyst Rohan Desai, major challenges include data integration, data quality, model interpretability, and ensuring clinical relevance.
