Algorithmic Diagnosis, Human Harm, Legal Accountability Evaded -By Fransiscus Nanga Roka

The hallmark of the hospital today is no longer as a sanctuary for human judgement but a laboratory of algorithmic authority. You are now trained on Artificial Intelligence (AI), which now helps, at times with finality, in diagnosing diseases, rationing treatments, and forecasting patient survival. But when these systems go awry, the fallout is not merely a glitch but the potential for human disaster. Lives are altered, or lost. And in the days that follow, one weird question hovers: who is to blame? Increasingly the answer is no one. This is a problem not of technology. It is a legal one.

Hospitals embrace these as cost effective AI systems, offloading the most critical components of clinical reasoning into the hands of proprietary algorithms sold primarily by private vendors. Due to institutional pressure, physicians are using these systems as tools for decision support, frequently without clear information on how decisions reached. At the same time, software companies hide behind licensing agreements, disclaimers and inscrutable machine learning models. And that results in a vacuum of accountability perfectly engineered.

These actions begin to breach traditional legal frameworks when an AI system misdiagnoses a potentially life-threatening condition. It is the basic assumption of medical malpractice law that a human actor exercising professional judgment. Product liability law presumes a defect in a physical product. A diagnosis based on artificial intelligence does not squarely fit into either category. It’s not just a tool with autonomous decision making capabilities, It exists in a legally convenient gray area where all parties can absolve themselves of responsibility.

Hospitals say they only use certified technologies. In defense, doctors say they depend on systems certified safe by their institutions. Vendors maintain their products are advisory and not determinative. Each position is individually plausible. Together, they create an accountability-less system. It is also not an accident such fragmentation is built into the structure itself.

From a law and economics perspective, the regime that is currently in place produces perverse incentives. The diffusion of liability decreases the expected cost of error for everyone involved. Vendors can deploy rapidly without incurring high legal risk proportional to the size of their customers backwards. Hospitals get to preen about new high-tech stuff while externalizing any potential harms. As such, physicians can no longer serve as independent decision makers but rather as conduits for institutional protocols. But the real cost is often one that patients pay with no clear routes to remedy.

The legal system is in theory supposed to assign blame and prevent negligence as a result of technology that it barely comprehends outpacing its integration by citizens with every passing pulse. Algorithmic opacity poses evidentiary problems for courts. Regulators are always one cycle behind in the innovation inducing incentives offered by the private sector. Legislators, usually not at all tech savvy themselves, come up with some vague principles that do nothing to address the basic questions of liability.

What is revealed isn’t just an gap, but a calculated absence that serves institutions and leaves individuals vulnerable.

That is something that should alarm anyone who cares about the basic tenets of the rule of law: accountability, deterrence and justice. A system in which harm occurs without accountability is not neutral; it is inherently unjust.

The answer, of course, is not to ban AI in medicine. Its potential benefits are undeniable. However, such integration must be balanced with a radical reimagining of legal responsibility. Hybrid accounts of liability akin to product liability and professional responsibility need to be created. The introduction of transparency standards for algorithmic decision-making should be a prerequisite. High-risk medical AI applications should be covered by a strict liability regime and this solution should not be deemed too excessive, but rather necessary.

Above all, the burden of uncertainty must be transferred from the patient to the system.

In the meantime, the dream of AI based healthcare will remain inherently unethical. Innovation at the expense of accountability is not progress, it is exploitation layered with a patina of technology.

And each misdiagnosis tucked behind an algorithm delivers a reminder: the law, when it declines to identify liability, quietly imposes the burden on those least able to absorb it.

Faculty of Law University 17 August 1945 Surabaya and Manager Partner Law Firm Victorious Indonesia