The Anaesthesia –AI Paradox : Weighing the drawbacks and the benefits

Dr. Shital Dalal

Assistant Professor, Dept. of Anaesthesiology
IGGMC, Nagpur

“MADE, NOT BORN”

Who first imagined the concepts of robots, automata, human enhancements, and Artificial Intelligence?

Two thousand years ago in fact, we will find a remarkable set of ideas and imaginings that arose in mythology, stories that envisioned ways of imitating, augmenting, and surpassing natural life by means of what might be termed biotech, “life through craft.” Long before the clockwork contraptions of the Middle Ages and the automata of early modern Europe, and even centuries before technological innovations of the Hellenistic period made sophisticated self moving devices feasible, ideas about making artificial life—and qualms about replicating nature—were explored in Greek myths. Beings that were ―Made, not Born‖ appeared in tales about Jason and the Argonauts, the bronze robot Talos, the techno-witch Medea, the genius craftsman Daedalus, the fire-bringer Prometheus, and Pandora, the evil fembots created by Hephaestus, the god of invention.

The myths represent the earliest expressions of the timeless impulse to create artificial life. These ancient ―science fictions‖ show how the power of imagination allowed people, from the time of Homer to Aristotle’s day, to ponder how replicas of nature might be crafted.

Thus Artificial intelligence (AI) is defined as the broad concept of machines designed to understand and perform tasks on their own in a ―smart‖ manner. The growing human resource crisis in healthcare today may be an ideal setting for using technology to fill in the gaps; starting with telemedicine, digital health platforms and progressing to the adoption of AI.

Rapid advances in Artificial Intelligence (AI) have led to diagnostic, therapeutic, and intervention-based applications in the field of medicine. Today, there is a deep chasm between AI-based research articles and their translation to clinical anaesthesia, Machine learning (ML), the most widely applied arm of AI in medicine, confers the ability to analyse large volumes of data, find associations, and predict outcomes with on-going learning by the computer. It involves algorithm creation, testing and analyses with the ability to perform cognitive functions including association between variables, pattern recognition, and prediction of outcomes.

AI-supported closed loops have been designed for pharmacological maintenance of anaesthesia and hemodynamic management. Mechanical robots can perform dexterity and skill-based tasks such as intubation and regional blocks with precision, whereas clinical decision support systems in crisis situations may augment the role of the clinician.

Possibilities are boundless, yet widespread adoption of AI is still far from the ground reality. There has been always positive side of AI research related to anaesthesia and perioperative care. One of the simpler ways in which a computer can be used in anaesthesia is to design a servo system, for instance, to maintain Bi-spectral Index (BIS) within a specified range by continuous assessment and adjustment of the infusion rate of the anaesthetics agent(s).

Prompt adoption of technology has been the hall mark of anaesthesia and is responsible for the significant increase in patient safety in this specialty over the last few decades. As a Paradox of Anaesthesia AI : It is” Made not Born” having benefits as well as its own drawbacks We have to weigh it as risk benefit ratio. We need to understand the benefits that AI can bring to clinical care, as it aims at assisting the Anaesthesiologist and not replacing them.

AI and Pre-anaesthetic Evaluation: Traditionally, logistic regression has been the basis of scoring indices widely applied for risk assessment in anaesthesia. This type of risk prediction is especially useful for counselling, optimization, and planning the anaesthetics management of individual cases with rare co-morbidities. The intrinsic ability to integrate pre, intra and postoperative data seamlessly makes it superior to traditional risk assessment modules. AI has been employed for airway assessment and encouraging results reported include ability to correctly predict the Cormack–Lehane view on direct laryngoscopy with analysis of face and neck. Preoperative checklists have contributed to increased surgical safety. CDSS for
selection of the most appropriate antibiotic and dosing schedule have been developed and widely tested in multi-centre trials.

AI and Intraoperative Anaesthetic Care: Applications of AI in the operation theatre include monitoring and alarm fatigue, administration of anaesthesia, hemodynamic management, and clinical decision support. Intraoperative cognitive robots can be integrated into alarm systems for simultaneous analysis of several parameters thereby lowering the rate of false alarms. There is a growing interest in monitoring the target organ of anaesthesia, the brain, via the electroencephalogram (EEG) as it may help in measuring the anaesthetics effect. Automated closed loop anaesthesia delivery (CLAD) involves hypnosis, analgesia, and muscle relaxant delivery systems incorporating hemodynamic feedback mechanisms being studied with promising results. Advances in non-invasive cardiac output monitoring, cerebral oximetry, EEG processing, and nociception assessment form the basis for CLAD. Intraoperative analgesia-nociception monitors are now available to titrate opioid administration based on changes in the sympathetic and/or parasympathetic systems. The pain indices available include Nociception level Index, Analgesia-nociception index, Surgical plethysmographic index, Pupillometry, and Pupillary pain index, each of which is measured by its respective monitor To pre-empt critical events, investigators have developed ―super learner‖ algorithms specifically trained to predict an acute hypotensive episode 10–30 minutes prior to the event, thereby allowing enough time for intervention.

Predictive therapy‖ refers to prediction of long-term outcomes with the intention to start pre-emptive therapy for effective use of resources. Rapid technological advances such as threedimensional ECG imaging as well as analysis of arterial waveforms have been used to provide clues to early detection of coronary insufficiency. The ability to predict and
investigate these rare but devastating complications may become critical for overall morbidity and mortality reduction.

AI and Postoperative Care :. Investigators have studied ataxic or irregular breathing pattern and developed a ML algorithm for quantifying breathing pattern to help with prediction of
respiratory depression in the postoperative period. Advances in telecommunication have led to the development of wireless intelligent patient-controlled analgesia for feedback-enabled
pain management in surgical homes.Early discharge and ambulation in patients can be closely monitored via video conferences and effective feedback mechanisms, impacting healthcare costs and patient satisfaction in a positive manner.

While AI has several potential benefits in anaesthesia, there are also potential drawbacks

1. Lack of data. AI relies on large amounts of data to make accurate predictions.
2. lack of standardization in anaesthesia practice.
3. Anaesthesiologists use different techniques and protocols, which can make it difficult for AI to make accurate predictions. So Anaesthesiologists need to be aware of the limitations of AI and be prepared to intervene if necessary.
4. Ethical and humanistic principles remain significant as new interventions relying on AI are developed using medical records by being outsourced to researchers or companies. The integration of AI in anaesthesia also raises ethical and legal questions. Who is responsible if an AI system makes an error that results in harm to a patient? How can we ensure that AI systems are transparent and accountable? What are the implications of using AI to make life-and-death decisions? This includes ensuring that AI systems are transparent and explainable, meaning that their decision-making processes can be understood by humans. Obviously, the anesthesiologist needs to take a call in a crisis to follow or ignore AI-based intervention with medicolegal implications
5. One of the ethical challenges in implementing AI based machine leaning in anaesthesiology is the issue of data privacy and confidentiality.
6. The use of de-identified data or obtaining informed consent from patients can be potential solutions to address this challenge.
7. Another ethical challenge is the potential bias in ML algorithms It is essential to develop guidelines and regulations for the use of AI in anaesthesia.

Improvement of anaesthesia provider’s productivity and patient outcome by using “augmented intelligence” based on pooled patient data as well as incorporation of clinical guidelines is the underlying goal for adoption of AI.In advanced nations, strong collaboration among clinicians, scientists, manufacturers, regulators, and administrators has led to consistent electronic health records amenable to data storage and exchange.

Most important , there is a concern of replacing human expertise with AI.
There are few paradox with AI like The AI power paradox ,The Great AI Paradox, The AI Paradox .Moravec paradox which states that computers can easily perform tasks that involve logical reasoning ,they struggle with tasks that involve sensory and motor skills. While AI can assist in decision making, it cannot replace the expertise and judgment of human anaesthesiologists. Therefore, it is important to ensure that the use of AI in anaesthesiology is complementary to human expertise and not a substitute for it. Thus this Paradox Anaesthesia -AI regarding Benefits and drawback we have to weigh by human Intelligence.

Adhyay 7: Knowledge Of Ultimate Truth Establishing Balance ( Bhagavad gita)