It is estimated that approximately 30% of the daily data volume generated globally is generated by healthcare-related (industries) (RBC Capital Markets). One of the biggest challenges inherent in the industry is that although the data is available, it is siloed. The pandemic of the novel coronavirus (COVID-19) infection has shed light on siloed data. From inaccurate case counts to inefficient information-sharing between hospitals on viral progression, the need for an effective data interoperability infrastructure became even more apparent. Data connectivity is expected to increase dramatically in the coming years due to US and European data strategies.

AI-based technological innovations are prominent as seen in the attention placed on large language models (LLMs) such as ChatGPT. Generative AI is also having a significant impact on healthcare. Pharmaceutical companies have already implemented this technology to generate synthetic patient data, design new proteins, and so forth. Through combining pre-trained, open, large-scale language models with AI models trained on clinical data collections, the technology is successfully structuring all kinds of items with high accuracy in areas such as diagnoses, medications, tests, and physical findings.

According to economic reports from several research firms, digital health is growing at a CAGR of nearly 20%, with some predicting that it will approach the pharmaceutical market by the 2030s.

Given this situation, in this article, we have taken a bird's-eye view based on the below structure of the "health and medical transformation" that is expected to significantly change the nature of society, citizens, health and medicine, and R&D in the future from the perspective of the relationship between digital technology and biotechnology.

Here are four highlights that emerged from this bird's-eye view.

• 1. Diverse Developments in Monitoring and Behavioral Change

  As shown by remote monitoring as a new form of medical treatment, distributed clinical trials, digital biomarkers, and digital therapies, despite differing purposes that range from prevention to treatment and forms of medical treatment, areas and markets that combine wearables, smartphones, cloud computing, and AI are attracting attention regardless of their purpose.

• 2. Utilization of Real World Data and Evidence for Drug Discovery

  A major trend is to combine various data groups such as electronic medical records and similar data generated in hospitals or heterogeneous data such as data from smartphones and smartwatches, genomics, and omics data to search for drug discovery targets. Heterogeneous data interoperation has become important for this purpose.

• 3. Utilization of Large-scale Language Models

  Large-scale language models are beginning to be used for the likes of clinical decision support in medical institutions and protein design in pharmaceutical research.

• 4. Rise of Programmatic Biology

  Synthetic biology is encountering the field referred to over time as AI drug discovery. In this article, the term "programmatic biology" is used. The trend toward the connection between target discovery and new drug development through programmatic biology as mentioned earlier can be observed herein.