Anaesthesia 2.0: Merging human expertise with AI

Anjali Rajan*, Mohana Rangam

Department of Anaesthesia, Kauvery Hospital, Radial Road, Chennai, Tamil Nadu

Background

Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare. Its applications range from diagnostic imaging to robotic surgery, and anaesthesia is increasingly becoming a key field of innovation. Anaesthesiology, which involves complex decision-making under time-sensitive conditions, stands to benefit significantly from AI integration.

1. Preoperative Assessment and Risk Prediction

AI-powered algorithms can analyze electronic health records, laboratory investigations, and imaging data to predict perioperative risks such as difficult airway management, hemodynamic instability, or adverse drug reactions (Hashimoto et al., 2020).

Predictive analytics has the potential to assist anaesthesiologists in stratifying patients more accurately and in developing personalized anaesthetic plans.

2. Personalised Anaesthesia Dosing and Closed – Loop Systems

Traditional drug dosing relies on standardized protocols that may not account for individual variability. AI-based closed-loop anaesthesia delivery systems continuously monitor physiological parameters—such as EEG, blood pressure, and oxygen saturation—and automatically titrate anaesthetic agents to maintain optimal sedation and analgesia (Liu et al., 2021). Such systems can reduce the risks of awareness during surgery or drug overdose.

3. Intraoperative Monitoring and Decision Support

AI can integrate data from multiple monitoring devices to detect patterns that may precede clinical deterioration. For example, machine learning algorithms have been developed to predict intraoperative hypotension up to 15 minutes before onset, enabling earlier intervention (Hatib et al., 2018). Decision support systems can also recommend fluid therapy, vasopressor use, or airway interventions, acting as a “second set of eyes” for anesthesiologist.

Airway: AI-assisted imaging–has provided more objective tools for airway assessment. Hayasaka et al. proposed a facial image-based intubation difficulty classification method. Another model incorporating attention mechanisms to extract Shim et al. developed a machine learning model based on elastic net regression to predict endotracheal tube depth in pediatric patients under 7 years old.

Ultrasound guided regional Anaepidural

ScanNav is an AI image enhancement device designed to highlight anatomical structures in real-time B-mode ultrasound images with an accuracy of 93.5%. Thus, reducing the risk of nerve damage and failure of block. Compagnone et al. evaluated a portable, handheld ultrasound device enhanced with artificial intelligence. The system integrated real-time ultrasound imaging with machine learning algorithms to automatically identify spinal anatomical landmarks. This AI-assisted device enabled clinicians to determine the optimal intervertebral insertion point and estimate the depth to the epidural.

4. Postoperative Care and Pain Management

Postoperative complications, including nausea, delirium, or uncontrolled pain, can be anticipated using AI-driven predictive models. Wearable sensors linked with AI can provide continuous monitoring in recovery units or even at home, facilitating early detection of deterioration (Shickel et al., 2019). AI-guided multimodal analgesia planning may also help optimize opioid use and minimize dependence.

5. Recent AI developments in the field of perioperative medicine

  • Identification of biomarkers and optimisation of anesthetic protocols based on predictive analysis.
  • Protien structure prediction and therapeutic protien design
  • Accelerating anesthetic drug discovery
  • Reshaping postoperative outcome by prediction of complications much earlier.

6. Will AI Be a Threat to Anaesthesiologists?

The rise of Artificial Intelligence (AI) in anaesthesia often sparks debate about whether it could eventually replace anaesthesiologists. While AI has made remarkable progress in areas such as closed-loop anaesthesia delivery, predictive nalytics, and intraoperative monitoring, most experts agree that AI is unlikely to eliminate the role of anaesthesiologists. Instead, it is more likely to redefine and enhance it.

6.1. Why AI Cannot Fully Replace Anaesthesiologists?

  • Complex Decision-Making: Anaesthesia involves not just drug dosing, but also anticipating surgical complications, managing emergencies, and adapting to rapidly changing scenarios. These require human judgment, intuition, and experience.
  • Patient Interaction: Anaesthesiologists provide reassurance, explain procedures, and address concerns—roles that AI cannot replicate.
  • Ethics and Accountability: If an AI system fails, responsibility still lies with a human clinician. Society is unlikely to accept fully autonomous systems in life-critical roles without oversight.

6.2. How AI Enhances the Role of Anesthesiologists

  • Decision Support: AI can act as a “co-pilot,” offering early warnings of hypoxia, hypotension, or arrhythmias.
  • Precision and Efficiency: Automated drug titration and monitoring free up time, allowing anaesthesiologists to focus on higherlevel clinical decisions.
  • Workload Management: With growing surgical demands and workforce shortages, AI may help anaesthesiologists manage more patients safely.

6.3. Potential Risks of Over-Reliance on AI

  • Deskilling: If machines take over routine tasks, clinicians may lose proficiency in manual skills.
  • Bias and Errors: Poorly trained AI systems could make unsafe recommendations.
  • Economic Pressures: In some healthcare systems, administrators might view AI as a way to reduce staffing needs, which could pose a perceived “threat” to job security.

6.4. The most likely future

Rather than replacing anaesthesiologists, AI is expected to augment them. The specialty may evolve, with clinicians focusing more on:

  • Interpreting AI recommendations
  • Complex case management
  • Ethical decision-making
  • Expanding roles in perioperative medicine and critical care.

Conclusion

AI is not a threat but a powerful tool. Just as pulse oximeters and capnography once transformed anaesthesia without replacing doctors, AI will become an integral part of practice. The anaesthesiologists who adapt—by learning to work alongside AI—will not only remain relevant but will provide safer, more efficient, and personalized care than ever before.

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