Can Artificial Intelligence Help During a Shoulder Dystocia Emergency?
When shoulder dystocia occurs, every second matters. The clinician must simultaneously recognize the impaction, call for help, recall the proper sequence of maneuvers, execute them correctly, communicate with the team, and document everything in real time—all while managing the cognitive load of a high-stakes emergency. Could artificial intelligence provide support during this critical window? The honest answer is: not yet, but the technology is emerging.
Currently, AI applications in shoulder dystocia focus primarily on prediction rather than real-time management. Machine learning models can analyze prenatal ultrasound data, maternal demographics, and labor characteristics to identify patients at elevated risk for shoulder dystocia before delivery occurs. A 2020 study developed a predictive model using multiple clinical parameters that could stratify risk, potentially allowing teams to prepare in advance. However, prediction alone doesn’t solve the fundamental problem: most shoulder dystocia cases occur without warning in patients without traditional risk factors.
The real opportunity lies in AI-enhanced cognitive support during the emergency itself. Consider what’s possible with current technology. An AI system could provide real-time prompting through the HELPERR algorithm, adapting to the specific clinical scenario as it unfolds. Voice-activated commands could trigger visual displays showing proper hand positioning for internal rotation or posterior arm delivery. Computer vision technology could analyze the delivery in real time, detecting when excessive traction is being applied and alerting the team. Natural language processing could convert verbal callouts into structured documentation automatically, eliminating the problem of retrospective charting after the emergency resolves.
Some of this technology already exists in other medical domains. Operating rooms use AI-powered systems that track surgical instruments, monitor sterile fields, and document procedural steps. Emergency departments employ clinical decision support tools that guide resuscitation protocols in real time. The challenge in obstetrics is adapting these technologies to the unique constraints of the delivery room—the unpredictability of timing, the need for immediate hands-free interaction, and the simultaneous demands of patient care and team coordination.
Virtual reality training represents the most mature AI application in shoulder dystocia management. Recent studies demonstrate that VR-based simulation can improve adherence to management algorithms and reduce diagnosis-to-delivery time. These systems use AI to adapt scenario difficulty, provide individualized feedback, and track skill retention over time. While this addresses the training gap, it doesn’t yet translate to assistance during actual emergencies.
The most promising near-term application may be AI-enhanced cognitive aids. Traditional shoulder dystocia checklists are static documents posted on delivery room walls—easy to ignore in the chaos of an emergency. An AI system could provide dynamic, context-aware guidance through wearable displays or room-mounted screens. It could track which maneuvers have been attempted, time each intervention, prompt the next step in the algorithm, and ensure proper documentation occurs. Critically, such a system would need to function as decision support, not autonomous control, maintaining the clinician’s judgment and expertise at the center of care.
The ethical and practical challenges are substantial. Any AI system in the delivery room must function flawlessly under time pressure, integrate seamlessly with existing workflows, and enhance rather than distract from clinical care. It must be trained on diverse datasets to avoid perpetuating existing disparities. And it must provide explainable recommendations that clinicians can understand and validate.
We’re not there yet. Current shoulder dystocia management relies on well-trained humans using systematic approaches—and as the simulation data demonstrates, when humans are properly trained and supported, outcomes improve dramatically. AI may eventually enhance that human performance, but only if we first commit to the foundational work of ensuring universal competency through mandatory simulation training, real-time documentation protocols, and standardized team-based care. Technology cannot compensate for inadequate training, just as checklists cannot replace competence.
What AI Could Tell, Call Out, and Document During Shoulder Dystocia
Routine Vaginal Delivery Monitoring (Pre-Dystocia Detection):
Announce “Fetal head crowning” when delivery imminent
Automatically initiate delivery timer at moment of head delivery: “Head delivered at 14:28:00, clock started”
Display elapsed time since head delivery continuously on room screen
Monitor for normal restitution and external rotation: “Restitution occurring normally”
Alert at 30-second mark if shoulders not delivered: “30 seconds since head delivery, shoulders not yet delivered”
Detect warning signs and announce: “Turtle sign observed - possible shoulder dystocia”
Monitor traction force if sensors available: “Normal traction force applied”
Announce if delivery progressing normally: “Anterior shoulder delivered, delivery progressing”
Initial Recognition and Activation:
Detect failure of shoulder delivery after head delivery and automatically announce “Shoulder dystocia recognized”
Initiate timer displaying elapsed time prominently for all team members
Automatically activate emergency response team and announce “Shoulder dystocia team activated”
Display HELPERR algorithm on room screens with highlighted current step
Call out “McRoberts position” and provide visual guide for proper leg positioning
During Maneuver Execution:
Announce each maneuver as it should be performed: “Initiating McRoberts maneuver”
Display proper hand placement for suprapubic pressure with anatomical overlay
Call out 30-second intervals: “30 seconds elapsed,” “60 seconds elapsed”
Prompt next step if current maneuver unsuccessful: “McRoberts unsuccessful, proceed to suprapubic pressure”
Provide voice-guided instructions for internal rotation: “Insert hand posteriorly, rotate anterior shoulder toward fetal chest”
Monitor for excessive traction and alert: “Warning: excessive downward traction detected”
Team Coordination:
Assign and announce team roles automatically: “Nurse Smith, apply suprapubic pressure,” “Dr. Jones, document maneuvers”
Prompt for additional personnel when needed: “Call anesthesia for potential maternal injury”
Remind team to communicate with patient: “Update patient on current status”
Coordinate closed-loop communication by tracking verbal acknowledgments
Real-Time Documentation:
Auto-timestamp each maneuver: “McRoberts initiated at 14:32:15”
Record sequence of maneuvers attempted: “Suprapubic pressure applied at 14:32:45”
Document head-to-body delivery interval continuously
Capture which specific maneuvers were performed and their duration
Record team member assignments and actions
Note any complications: “Posterior arm delivery successful at 14:33:30”
Generate preliminary delivery note with all timed events immediately after delivery
Decision Support:
Suggest next maneuver based on algorithm and elapsed time: “Consider posterior arm delivery if suprapubic pressure unsuccessful”
Alert if unconventional maneuver attempted: “Fundal pressure contraindicated in shoulder dystocia”
Prompt escalation: “At 4 minutes, consider all-fours position or Zavanelli maneuver”
Remind team of less common maneuvers if standard approaches fail: “Consider Woods screw maneuver”
Post-Delivery:
Summarize total head-to-body delivery time: “Total delivery interval: 3 minutes 45 seconds”
List all maneuvers performed in sequence with timestamps
Prompt neonatal assessment: “Initiate neonatal examination for brachial plexus injury”
Prompt maternal assessment: “Examine for fourth-degree laceration and postpartum hemorrhage”
Generate comprehensive structured note for medical record
Flag case for quality review if delivery interval exceeded threshold or injury occurred
Learning and Quality Improvement:
Compare actual performance to protocol adherence
Identify deviations from recommended sequence
Provide team performance metrics for debriefing
Anonymously contribute data to institutional outcomes database
Trigger automatic peer review for cases with adverse outcomes