2025 PA Med Mal Scholarship Winner
Veda Vinod
Introducing AI in Obstetrics and the Prevention of Birth Injuries
Imagine a delivery room humming softly with machines. A fetal monitor blinks. An algorithm runs silently in the background, scanning heart rate variability faster than any human eye. Artificial intelligence and machine learning have arrived in obstetrics not with drama but with quiet confidence. They now assist clinicians in prenatal screening, ultrasound interpretation, labor monitoring, and post delivery surveillance. These systems promise to predict complications such as fetal distress, shoulder dystocia, or delayed labor progression before they escalate into irreversible birth injuries.
Hospitals increasingly deploy AI to flag risk factors early, to prioritize high risk pregnancies, and to guide interventions when minutes matter. In theory, this predictive power reduces human error and prevents devastating outcomes such as hypoxic ischemic encephalopathy. In practice, it also introduces a new actor into the delivery room, one that never sleeps, never panics, and never testifies in court.
Redefining the Standard of Care Through AI Use
The law has long measured medical negligence by asking what a reasonable physician would do. That question grows more complicated when physicians consult machines that claim superior judgment. When an obstetrician relies on AI recommendations during a high risk delivery, the standard of care quietly shifts. AI moves from being a helpful tool to an implied benchmark.
Courts have faced similar transitions before. Electronic fetal monitoring was once novel, then optional, and eventually expected. AI appears to be following the same arc. As these systems become more widespread, failing to use them may invite allegations of outdated practice, while following them too closely may invite accusations of blind reliance.
Cases such as Bolam and Bolitho remind us that professional judgment must remain reasonable and defensible. AI does not replace that judgment. Instead, it reshapes the expectations surrounding it. The clinician remains the decision maker, but now operates in conversation with an algorithm whose authority can feel persuasive, even irresistible.
Confronting Risks and Errors in AI Driven Birth Care
Technology promises clarity, yet it often delivers complexity. AI systems learn from data, and obstetric data reflects decades of inequity, incomplete records, and human bias. When flawed data trains an algorithm, the errors scale quickly. A missed risk factor or misread fetal pattern can cascade into delayed intervention and preventable injury.
Overreliance poses another danger. In the intensity of labor and delivery, clinicians may defer to AI outputs even when instinct signals concern. Automation bias thrives in moments of stress. If AI fails to flag distress or falsely reassures the care team, precious time slips away.
Unlike human mistakes, AI errors resist explanation. Many systems operate as black boxes. When something goes wrong, neither physician nor patient may understand why. That opacity complicates clinical accountability and fuels litigation when families seek answers after a traumatic birth.
Untangling Legal and Liability Challenges in Birth Injury Cases
Birth injury malpractice has always focused on human conduct. AI disrupts that focus. When an injury occurs, plaintiffs now ask new questions. Did the physician rely too heavily on AI? Did the hospital implement a flawed system? Did the developer design unsafe software?
Traditional malpractice doctrine places liability on the provider who owes the duty of care. Product liability law, however, opens the door to claims against AI developers under theories of defective design or failure to warn. Greenman established that manufacturers bear responsibility for dangerous products. Whether courts will treat AI software the same way remains unsettled.
For now, most legal frameworks keep clinicians at the center of liability. Courts view AI as a tool, not an autonomous actor. Yet as AI grows more independent and influential, this legal fiction strains credibility. Shared liability models may emerge, reflecting the shared causation between human judgment and machine guidance.
Wrestling With Ethical Questions in AI Assisted Birth Care
Ethics enters the delivery room quietly but forcefully. Patients rarely know when AI influences their care. Consent forms mention risks of surgery and anesthesia but not algorithms shaping clinical decisions. True informed consent requires transparency, and transparency remains elusive when AI logic cannot be easily explained.
Trust also hangs in the balance. Patients trust physicians because they believe someone is accountable. When decisions feel outsourced to machines, that trust weakens. Clinicians, too, must guard against emotional distancing. Birth is deeply human. Ethical care demands presence, responsibility, and humility, even when technology whispers certainty.
After an injury, ethical lapses often become legal ones. Lack of disclosure, confusion about decision making, and perceived evasion of responsibility can harden claims and inflame juries.
Recommending Policy and Regulatory Safeguards
To harness AI safely, policy must act with foresight rather than regret. Regulators should treat AI systems used in obstetrics as high risk medical tools subject to rigorous testing, validation, and ongoing oversight. Static approval is not enough for software that learns and evolves.
Hospitals should adopt clear protocols defining how clinicians may use AI and when human judgment must override automated guidance. Documentation of AI recommendations and clinician responses should become standard practice, protecting both patients and providers.
Education matters just as much as regulation. Clinicians must learn not only how to use AI but how to challenge it. Skepticism is not resistance. It is responsibility.
Envisioning the Future of AI and Birth Injury Malpractice
The future will not banish AI from obstetrics, nor should it. Used wisely, AI can reduce errors, spotlight hidden risks, and save lives. Used carelessly, it can amplify mistakes and obscure accountability.
Birth injury malpractice will evolve alongside this technology. Lawyers will interrogate algorithms. Courts will reinterpret standards of care. Policymakers will race to keep pace. The challenge is not to slow innovation but to guide it with ethics, transparency, and law.
If AI is to assist in bringing life safely into the world, it must remain a servant, not a sovereign. The law must ensure that when machines enter the delivery room, responsibility never leaves it.
References
- Bolam v. Friern Hospital Management Committee, [1957] 1 WLR 582.
- Bolitho v. City and Hackney Health Authority, [1998] AC 232.
- Greenman v. Yuba Power Products, Inc., 59 Cal. 2d 57 (1963).
- Price, W. N. (2019). Medical malpractice and black box medicine. Harvard Journal of Law and Technology, 33(1), 113–165.
- Gerke, S., Minssen, T., and Cohen, G. (2020). Ethical and legal challenges of artificial intelligence driven healthcare. Cambridge Quarterly of Healthcare Ethics, 29(4), 543–556.
- Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
- U.S. Food and Drug Administration. Artificial Intelligence and Machine Learning in Software as a Medical Device.
