The world around us is rapidly changing due to the emergence of novel disruptive technologies like Artificial Intelligence (AI). These technologies have profoundly altered the structure of various industries and the competitive landscape among companies, making them impossible for firms, businesses, and policymakers to ignore. Consequently, numerous studies in recent years have introduced new approaches for identifying emerging technologies and the resulting developmental opportunities. However, this field of study still faces challenges, such as reliance on expert knowledge, focus on a single data source, and the lack of a comprehensive evaluation and prioritization of technological opportunities. Addressing these gaps, the present research employs a data-driven approach to identify and evaluate AI technology opportunities in the healthcare sector. For this purpose, article and patent data were collected from two credible databases, and key topics were extracted using BERT-based Topic Modeling. These topics were then evaluated using metrics such as growth rate, frequency, citations, and network analysis indicators, and subsequently ranked using the TOPSIS method. Finally, the identified opportunities were mapped onto a two-dimensional space and classified into four categories: Core and Proven Opportunities, Knowledge-Based and Emerging Opportunities, Engineering Innovations, and Low-Priority Opportunities.