[Contribution] Strategic inflection point for healthcare companies after the implementation of the AI ​​Framework Act

Moving beyond compliance with licensing to an era of ‘medical justice risk’ management
Attorney Seohyung Lee of Daeryun Law Firm (Limited)

Since the implementation of the Basic Act on the Development of Artificial Intelligence and Creation of a Foundation for Trust (hereinafter referred to as the Basic Act on AI), the pharmaceutical, bio, and digital healthcare industries have been freed from regulatory uncertainty to some extent. This is because the government flexibly interprets the scope of application of high-impact AI directly related to life and health in consideration of the promotion of the healthcare industry and innovation, and operates with relatively limited regulations on diagnostic assistance solutions that require the intervention of medical professionals such as doctors and pharmacists.

This policy approach is a reasonable measure to maintain the momentum of digital healthcare technology development. In particular, the administrative regulatory system in the healthcare field is also firmly established, with a customized Ministry of Food and Drug Safety approval track for software medical devices (SaMD) being launched through the Digital Medical Products Act, which has been implemented in earnest.

However, stabilization of this regulatory environment does not mean resolution of legal risks. Rather, the judicial responsibility of medical AI is likely to be put to the test in earnest from now on.

◆ Legal gap between formal intervention and actual control in clinical settings

Some digital healthcare companies may try to overcome the threshold of high-impact AI regulations by requiring approval from medical staff or pharmacists at the final stage of their AI solutions. However, when a medical dispute arises and becomes the subject of a court's judgment, the focus is not on the 'existence of medical staff intervention' but on whether that intervention performed a 'substantial clinical control function' directly related to the patient's life.

For example, let's assume that an early cancer diagnosis AI failed to learn enough data from female patients of a certain age and misdiagnosed a malignant tumor, or that an anticancer drug dosage recommendation algorithm underestimated liver function levels, resulting in serious side effects. At this time, the court does not simply look at whether ‘the medical staff pressed the final approval button.’ We will comprehensively determine whether the AI ​​provided clinical evidence and explanatory potential at a level that medical staff can reasonably review, and whether a control system to cross-verify algorithmic bias and errors exists within the company.

If the system is designed so that the approval process is carried out mechanically without substantive verification without considering the characteristics of busy clinical sites, the so-called human intervention may be reversed as a situation showing a fatal flaw in the product safety management system, rather than as a logic of immunity for healthcare companies. This can be expanded to governance risk, where the issue is whether management has established and supervised a reasonable internal control system. If significant patient damage occurs, the possibility that this may lead to a shareholder lawsuit or violation of the board of directors' supervisory obligations cannot be ruled out.

◆ A new standard in global Big Pharma partnerships and overseas B2B markets

As of 2026, with the full-scale application of the EU AI Act (Artificial Intelligence Act), the global healthcare market is already demanding the highest level of medical AI governance. When global multinational pharmaceutical companies (Big Pharma) or large medical institutions discuss new drug material technology exports or joint clinical contracts with K-healthcare companies, they do not simply ask whether they comply with the Korean Ministry of Food and Drug Safety guidelines. There is a trend to request specific data to prove the legitimacy of sensitive patient medical data used for learning, transparency of prediction algorithms, and company-wide bioethics control system.

The fact that domestic regulations can be avoided is not a sufficient explanation to strict foreign partners or regulatory agencies (FDA, EMA, etc.). Rather, if the internal medical AI governance system is insufficient, it may lead to exclusion from the global healthcare supply chain as well as acceptance of very fatal Representations & Warranties clauses in contracts. Now, in the pharmaceutical and healthcare industries, AI governance is an absolute prerequisite for accessing the global market.

◆ Challenges for healthcare companies in the post-AI basic law era

Pharmaceutical, bio, and digital healthcare companies must abandon the defensive approach of avoiding regulations and switch to a strategy of securing sustainable competitiveness based on patient safety and trust. Reactive response after a problem occurs is a cost, but establishing a control system at the design stage is close to a strategic investment that protects corporate value. To this end, we propose the following tasks:

① Systemization of verifiable medical responsibility

An audit log must be established from the product planning stage in which medical staff or researchers at clinical sites review AI-derived results and systematically record the process of modifying or rejecting AI-derived results when necessary based on medical judgment in the EMR (electronic medical record) or clinical research system. This will be a key defense tool to prove that companies and management have fulfilled their duty of reasonable care in future medical disputes or shareholder lawsuits due to clinical failures.

② Reorganization of contract structure for the entire medical AI value chain

Risks must be clarified when using an external foundation model to develop new drugs and digital therapeutic devices or when supplying AI solutions to hospitals and pharmacies. It is essential to determine how to distribute responsibility due to technical defects, misdiagnosis, and sensitive medical data leaks, and to establish a sophisticated risk allocation contract structure between the solution provider and the introducing institution, such as hospitals.

③ Establishment of a Compliance by Design system that prioritizes patient safety

In healthcare companies that deal with life, AI risk is not just a problem for a specific IT development department, but a company-wide governance issue that will determine the survival of the company. A governance structure is required that establishes a control system in which Medical Affairs, RA, Legal, and Information Security departments participate from the beginning of pipeline planning and solution development, and manages this as a key agenda at the board level at all times.

◆ Beyond the illusion of legality, to the premium of patient trust

The government's flexible interpretation of regulations provides healthcare companies with a golden time for technological innovation, and does not relieve them of the strict obligation to manage risks directly related to patients' lives. In the era of the 2026 AI Basic Law, the true competitiveness of K-Bio and digital healthcare does not come from narrowly crossing the boundaries of licensing regulations.

Only companies that proactively establish transparent and verifiable medical AI governance can be evaluated by global partners and patients as a partner they can trust with their lives and health. Solid AI governance is not a sunk cost, but the best strategic capital allocation that protects the company's intrinsic value and patient safety.

The challenges facing the healthcare ecosystem following the implementation of the AI ​​Framework Act are clear. Legality is only the minimum standard. Patient trust is not gained through licensing, but rather is proven by what controls, records, and accountability structures the company had in place at the moment of conflict.

|Contribution| Attorney Seohyung Lee of Daeryun Law Firm (Limited)

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[Contribution] Strategic inflection point for healthcare companies after the implementation of the AI Framework Act (link)