Healthcare organizations are installing artificial intelligence tools across their operations — diagnostic algorithms, predictive models, patient monitoring systems — while avoiding a fundamental question: Who gets sued when these systems harm patients?
Healthcare organizations bear full legal responsibility for any patient harm caused by AI tools they choose to use. This is direct medical malpractice liability, whether the AI was built internally or bought from a vendor. Yet while executives discuss AI’s benefits, almost no one addresses the malpractice risks of integrating these technologies into patient care.
This silence creates a dangerous blind spot. AI-related liability claims are already appearing in courtrooms, and healthcare leaders remain largely unprepared for the legal consequences of their AI adoption decisions.
The solution isn’t to avoid AI. Instead, healthcare organizations should approach implementation with the same clinical rigor and risk management protocols that they have successfully applied to other medical technologies. Organizations that address liability proactively, rather than reactively, will build successful, sustainable AI programs that protect patients and institutional assets.
The Legal Reality Healthcare Leaders Are Ignoring
The liability framework leaves no room for confusion. Medical groups are completely responsible for patient care outcomes, including those involving AI systems. If we introduce a tool or technology that harms a patient, that’s our medical malpractice. Vendor indemnification clauses and regulatory approvals don’t change this accountability.
A 2021 U.S. study of 2,000 potential jurors found that physicians who accept AI recommendations for nonstandard care face increased malpractice risk. Jurors judge physicians on two factors: 1. Did the treatment follow standard protocols? 2. Did the physician accept the AI’s advice?
When AI recommends departing from established care, physicians face legal risk either way.
The situation gets murkier with predictive systems. If an algorithm identifies a patient needing immediate attention, but that patient isn’t contacted and suffers harm, who’s liable? Courts haven’t settled whether healthcare organizations must act on every AI-generated alert.
Recent litigation reveals where problems cluster. A 2024 analysis of 51 court cases involving software-related patient injuries shows three patterns:
- Administrative software defects occur in drug management systems.
- Clinical decision support errors happen when physicians follow incorrect AI recommendations.
- Embedded device malfunctions affect AI-powered surgical robots and monitoring equipment, though these cases often involve shared liability between healthcare organizations and device manufacturers.
Each scenario represents different risks that organizations must assess before deploying AI tools.
Why Current AI Strategies Amplify Risk
Most healthcare organizations are deploying technology they don’t fully understand for decisions that affect people’s lives. The “AI” label creates a dangerous assumption that these systems possess human-like reasoning abilities. They don’t. These are probability engines that predict the next most likely outcome based on patterns in training data. When organizations remove their normal product development guardrails because something carries the AI brand, they create unnecessary liability exposure.
The financial calculations behind many AI deployments reveal flawed decision-making. Organizations are spending thousands per user annually for tools that save clinicians just five minutes daily. While these savings can add up across large user bases, one malpractice lawsuit from an AI-related incident could eliminate years of marginal productivity gains. Many organizations aren’t factoring liability risk into their ROI calculations, focusing only on time savings without considering the legal exposure they’re creating. These incomplete financial assessments will face scrutiny during contract renewals, and market corrections will eliminate tools that can’t demonstrate real value when liability costs are included.
Perhaps most concerning is the widespread confusion about what current AI actually does well. Most AI tools excel at operational tasks like scheduling, resource allocation, and transcription. They struggle with complex clinical reasoning that requires understanding context, patient history, and nuanced medical judgment. The problem is that these systems communicate in fluent, coherent language, creating a false impression of intelligence and clinical reasoning ability. Organizations continue overestimating these systems’ capabilities for direct patient care applications, mistaking sophisticated language processing for actual medical understanding.
This misalignment between AI capabilities and deployment strategies creates liability risk. When organizations deploy operational tools for clinical decisions or assume AI can replace human judgment in complex medical scenarios, they set themselves up for the exact situations that generate malpractice claims.
A Risk-Mitigation Framework
Healthcare organizations can reduce AI liability exposure through a structured approach that prioritizes safety over speed. The key is building competency with low-risk applications before expanding to clinical decision-making. Here are the essential steps:
- Start administrative, avoid clinical: Deploy AI first for scheduling, resource allocation, and transcription where errors create operational problems, not patient harm. Build organizational expertise before moving to clinical applications involving patient care decisions.
- Match capacity to deployment: Don’t implement systems that create problems you can’t solve. If you can see 100 patients weekly, don’t deploy AI that identifies 2,000 needing immediate care. This creates liability when you can’t respond to recommendations.
- Establish oversight protocols: Create clinical committees to evaluate every AI deployment. Document all decision-making processes and maintain audit trails showing whether recommendations were accepted or rejected. This documentation becomes critical in malpractice cases.
- Choose vendors strategically: Prioritize established companies integrating AI into existing workflows over point solutions. Demand outcome-based pricing where vendors share financial risk for promised results.
- Prepare legally: Review malpractice insurance for AI coverage gaps and educate staff on liability implications. Most policies weren’t written with AI in mind.
The goal isn’t to avoid AI but to implement it with the same clinical rigor healthcare applies to other medical technologies. Organizations that take these steps now will be positioned to scale AI responsibly as the technology matures, while those that ignore liability risks may find themselves defending decisions they never properly evaluated.
About Andy Flanagan
As CEO, Andy Flanagan is responsible for Iris Telehealth’s strategic direction, operational excellence, and the cultural success of the company. With significant experience in all aspects of our U.S. and global healthcare system, Andy is focused on the success of the patients and clinicians Iris Telehealth serves to improve people’s lives. Andy has worked in some of the largest global companies and led multiple high-growth businesses providing a unique perspective on the behavioral health challenges in our world. Andy holds a Master of Science in Health Informatics from the Feinberg School of Medicine, Northwestern University, and a Bachelor of Science from the University of Nevada, Reno. His prior experiences include being a three-time CEO, including founding a SaaS company and holding senior-level positions at Siemens Healthcare, SAP, and Xerox.
