“If Machines Exceed Us: Health Care at an Inflection Point,” published in NEJM AI, reviews the revolution happening with AI technologies finding their way from all angles into health care. In this perspective, authors including Eyal Klang, M.D., from the Icahn School of Medicine at Mount Sinai and Tel Aviv University present promise and explore challenges of progress with AI in health care and thereby open a forward-looking platform to integrating AI into health care practice.

A New Era in AI in Healthcare: From Supportive to Superhuman

Traditionally, Artificial Intelligece (AI) in healthcare was seen more as an assistant to expand diagnostic capabilities, facilitate improved data management, and assist in decisions. The article identifies a watershed moment with the advent of ChatGPT and GPT-4 when expectations shifted towards autonomous systems capable of outperforming human experts in a variety of tasks, from diagnostics to treatment planning and even cognitive empathy​.

The other two advanced forms of AI include Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI). Both systems are thought to be more intelligent than humans one day, superseding clinicians in domains of care that range from very complex medical interpretation to culturally sensitive cognitive empathy. The article highlights the theoretical capability of ASI to reason independently and execute nuanced healthcare decisions beyond human capacity because of our cognitive limitations and sensory constraints.

AI has the power to reshape patient care. 

Unique advantages of AGI/ASI include:

Molecular-level preciseness: The work in ASI may be extended for molecular diagnostics and nanomedical applications that will enable the capacity for early diagnosis of diseases, with precisely targeted treatment intervention.

Predictive altruism-disparities mitigation: With deeper predictive algorithms, ASI might target resource distribution to areas where the impact is most likely to be realized, thus continuously learning about equity in health.

Enhanced Cognitive Empathy: Drawing on the behavioural data, ASI would tailor the responses to best meet the emotional and cultural needs of each individual patient, thus providing maximum engagement and overall satisfaction.

Innovative drug development: AI-powered simulations could revolutionize pharmacological research by simulating drug interactions in a high-throughput manner, thereby accelerating the discovery of effective therapies.

Deep data analytics at scale, predictive modelling, and molecular precision as a combination raises a new paradigm in health care management-decision making based on a wealth of data: higher precision in efficacy by diagnosis, treatment, and management. For personalized medicine, for example, the ASI has the potential to provide integration and analysis of multimodal data sources unprecedented in permitting customization of treatment plans.

Challenges and Ethical Concerns

The authors provide a truly evocative discussion of the ethical implications of ASI in health care, noting how the undeniable development of AI autonomy might result in a splitting of ethical frameworks. Ethical precepts in health care are based upon human values and constraints. However, as the AI systems begin to outperform their human counterparts in cognitive abilities, their conceptualizations of what would constitute an ethical construct, such as human autonomy or compassion, would differ in such ways as to sharply contrast from conventional thinking.

For example, AI’s approach to ethical dilemmas might conflict with human-centered approaches to considerations of autonomy or quality of life, which regulatory frameworks should evolve to protect.

Operationalizing AI within Clinical Environments

Current regulations, designed from a human perspective, are bound to be inadequate in addressing these advanced AI systems. The authors, therefore, emphasize that not only should AI technologies grow and evolve, but also the regulatory and research infrastructure evolve with the nature of the technology. Indeed, outcome-based assessment frameworks underpinned by automated A/B testing would enable AI-driven systems to be tested for safety and efficacy even as their internal reasoning mechanisms become complicated and non-transparent.

Preparing for Paradigm Shift

The article calls for the collaboration of health professionals, policy thinkers, and technology experts as a proactive approach to prepare for a future whereby AI systems will redefine clinical roles. This approach is anticipatory and encourages the health stakeholders to maintain a balanced view on the transformative potential of AI systems while at the same time ensuring that their ethos will support ethical and human-centered values.

Conclusion

The insights in this article are invaluable, placing into relief the need to adapt healthcare to the challenges and opportunities brought about by the coming of AGI and ASI. The foresight, detailed analysis, and consideration of technological and ethical dimensions by the authors provide a very good foundational framework for further discussion in healthcare innovation. I also commend their contributions to NEJM AI, as they set a blueprint for thoughtful driving of AI advances with patient-centric care.

Dr. Prahlada N.B
MBBS (JJMMC), MS (PGIMER, Chandigarh). 
MBA (BITS, Pilani), MHA, 
Executive Programme in Strategic Management (IIM, Lucknow)
Senior Management Programme in Healthcare Management (IIM, Kozhikode)
Postgraduate Certificate in Technology Leadership and Innovation (MIT, USA)
Advanced Certificate in AI for Digital Health and Imaging Program (IISc, Bengaluru). 

Senior Professor and former Head, 
Department of ENT-Head & Neck Surgery, Skull Base Surgery, Cochlear Implant Surgery. 
Basaveshwara Medical College & Hospital, Chitradurga, Karnataka, India. 

My Vision: I don’t want to be a genius.  I want to be a person with a bundle of experience. 

My Mission: Help others achieve their life’s objectives in my presence or absence!

My Values:  Creating value for others. 

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