Kally Wong
Biography
Kally Wong is a healthcare business development leader with over two decades of experience driving growth across pharmaceuticals, biotech, and MedTech in both developed and emerging markets. With a proven track record in international market expansion, strategic partnerships, and commercialization, Kally has successfully led initiatives that translated innovation into Mscalable business outcomes. Having given guest lectures to the universities and peer-reviewed publications on market access in emerging economies, Kally offers deep insight into healthcare policies, reimbursement landscapes, and global market trends. Currently, her focus lies in analyzing how artificial intelligence can unlock new efficiencies and value in the healthcare sector?from accelerating drug development pipelines to enhancing diagnostic accuracy and clinical decision-making. By combining industry expertise with a forward-looking view on digital transformation, Kally brings audiences a clear lens on where the next wave of healthcare innovation and opportunity lies.
Research Interest
Traditional research often operates within disciplinary silos, limiting opportunities for cross-domain innovation, Additionally, the capital-intensive infrastructure required for cutting-edge AI research risks exacerbating disparities between large pharmaceutical companies and smaller biotech firms, further entrenching global inequities in drug development.
Abstract
The Double-Edged Sword: Artificial Intelligence in Drug Discovery:
The integration of artificial intelligence (AI) into pharmaceutical research marks a pivotal shift in drug discovery, offering both transformative opportunities and significant challenges. Platforms such as Insilico Medicine and Tempus demonstrate how machine learning algorithms can accelerate the development process by rapidly analyzing vast datasets, predicting molecular interactions, and optimizing compound selection?tasks that traditionally took years of experimentation. These efficiencies not only reduce R&D costs but also expedite the progression of promising candidates into clinical trials.
Importantly, AI has the capacity to generate novel biological insights beyond the reach of human expertise. Traditional research often operates within disciplinary silos, limiting opportunities for cross-domain innovation. In contrast, AI models?unconstrained by specialization?can uncover hidden correlations across genomics, proteomics, and chemical structures. This enables the identification of unconventional drug targets and therapeutic strategies that might otherwise remain undiscovered.
However, the application of AI in drug discovery is not without risks. Key concerns include algorithmic bias, overfitting, and the opaque ?black box? nature of many models, which often lack mechanistic transparency. Perhaps most critically, the quality and diversity of training data present a major vulnerability. Many AI systems are trained on datasets predominantly sourced from high-income countries, raising concerns about the representativeness of the data and the generalizability of the results. As a consequence, AI-generated solutions may underperform?or even mislead?in lower-resource settings, where genetic profiles, healthcare infrastructures, and disease burdens differ markedly.
Moreover, regulatory frameworks for AI-discovered compounds remain underdeveloped. There is no standardized method for validating the quality or completeness of the datasets underpinning these innovations. This creates uncertainty around the use of AI-derived compounds, particularly in rare diseases where patient populations are small and clinical trial design is inherently complex. Additionally, the capital-intensive infrastructure required for cutting-edge AI research risks exacerbating disparities between large pharmaceutical companies and smaller biotech firms, further entrenching global inequities in drug development.
To fully realize AI's promise in drug discovery, the industry must confront these challenges head-on?by fostering globally representative datasets, enhancing model interpretability, and establishing clear, robust regulatory guidelines. Striking a balance between innovation, equity, and scientific rigor will be critical as AI continues to reshape the future of medicine.
