By Chu Canh Chieu AI has become the engine transforming healthcare and life sciences from the ground up. What once took days or even weeks, such as clinical data analysis, diagnosis support, and drug screening, now happens in minutes, sometimes seconds. The clock is being rewritten.
Home to 54% of the clinical trials planned globally, Asia Pacific (APAC) holds a powerful edge to integrate AI to enhance drug discovery and development globally. By automating complex tasks and streamlining workflows, AI is not only enhancing efficiency for daily healthcare operations but also the key driver for research to confront current and future healthcare challenges.
Accelerating confirmation for superbug hypotheses
Antimicrobial resistance (AMR) is escalating into one of the most urgent global health threats. As bacteria, fungi, and viruses evolve resistance to once-effective drugs, “superbugs” are emerging—organisms immune to multiple antibiotics. The World Health Organization (WHO) and Asia-Pacific health leaders are spearheading global efforts to confront this crisis, with the WHO Western Pacific Region alone projected to incur up to US$148 billion in economic losses due to AMR between 2020 and 2030.
Yet, finding a solution remains difficult. The genetics behind resistance are complex, and bacteria evolve at a pace that outstrips traditional research. Identifying the mutations responsible demands the analysis of massive datasets—a slow, manual, and resource-intensive process. Even after potential resistance mechanisms are flagged, validation can take years, delaying critical breakthroughs.
To this end, AI has the potential to significantly accelerate the pace of AMR research by generating hypotheses and identifying genetic mutations linked to resistance more efficiently. AI algorithms can quickly process large datasets, detect patterns, and predict the emergence of resistance mechanisms to reduce the time required to analyse large datasets. By applying machine learning models to these datasets, AI can uncover hidden connections between bacterial strains, resistance pathways, and environmental factors that contribute to the spread of resistance.
A team of researchers at Imperial College London spent more than a decade examining the genetic mechanisms behind antimicrobial resistance (AMR), showing how bacteria exchange genetic material to rapidly spread resistance. Despite their findings, fully addressing AMR remained a major challenge, prompting the team to explore the potential of AI for deeper insight.
Partnering with Google, they piloted an “AI co-scientist” system using Google’s Gemini models. Designed to generate hypotheses and surface new angles on complex problems, the system was given just a few sentences on bacterial resistance. Within 48 hours, the AI produced multiple theories—one of which matched the team’s unpublished hypothesis—while also suggesting new directions for future research.
While the AI could not conduct experiments directly, its rapid generation of data-backed hypotheses allowed researchers to focus on experimental validation, which significantly accelerated their progress. In essence, AI serves as a powerful tool for human scientists, enhancing the speed and effectiveness of their research. Rather than replacing scientists, AI enables them to explore new opportunities in medical research and offers a promising solution to the AMR crisis.
Resolving talent shortage and manual tasks with AI-powered text transcription
Many countries across the APAC region are facing a critical shortage of healthcare professionals, with most falling short of the OECD average of 3.8 physicians per 1,000 people. Operational inefficiencies further aggravate this shortage, as healthcare professionals spend significant time on administrative tasks such as transcribing medical recordings. Indeed, transcription often takes hours to convert a short audio clip into accurate written documentation due to complex medical terminology and the need for precision.
One healthcare leader faced significant challenges related to documentation inefficiency and clinician burnout. After implementing FPT’s AISribe, an innovative AI tool to automate documentation processes, the organisation achieved a remarkable 50% reduction in healthcare documentation time to around 7 minutes per patient encounter. This automation tool also enhanced healthcare worker productivity, which reduced burnout and fatigue by 70%. Additionally, documentation accuracy improved, with 75% of physicians reporting higher quality in their clinical records.
Faster data analysis within seconds to improve healthcare quality
Despite producing vast volumes of information from patient records to clinical research, healthcare institutions still leave up to 70% of their data untapped, largely because the information is complex, fragmented, and time-intensive to process.
Unlocking these insights requires smarter, faster analytics. New platforms are emerging that combine Generative AI, Large Language Models, and conversational interfaces to simplify how data is analysed. These tools allow users to pose natural-language questions and receive insights in seconds without coding or advanced analytics training required. By eliminating the technical barrier, these solutions reduce costs, accelerate decisions, and make data-driven healthcare more accessible.
One such solution is Power Insights, an AI-driven platform built on Microsoft’s Azure OpenAI technology. By combining Generative AI with large language models, it enables users to extract actionable insights through simple, conversational prompts without the need for advanced analytical tools or SQL knowledge.
Delivering real-time analysis in under 30 seconds and supporting outputs in text, tables, and charts across more than 120 languages, the platform empowers clinical and operational teams to uncover patterns, optimise resources, and improve outcomes.
Looking ahead, the APAC healthcare and life sciences sector is entering a defining era, where AI doesn’t just support existing processes, but reimagines what’s possible. By accelerating insights, breaking down complexity, and expanding access to innovation, AI has the potential to not only fast-track discoveries but fundamentally reshape how care is delivered. To seize this opportunity, the priority must now shift to responsible implementation, ensuring that the immense power of AI is matched by a deep commitment to data privacy, ethical use, and global collaboration.
Matthew Brady