Will Artificial Intelligence Obviate the Need for Medical Doctors in the Near Future?

INTRODUCTION There is involvement of artificial intelligence (AI) in every sphere of life, including in health care. In fact, AI is impacting virtually all aspects of health care, from diagnostics to therapeutics, impacting clinical decision-making and patient management. We are witness to extensive computer-aided detection in radiology and to large language models capable of synthesizing medical literature in a matter of just minutes. With rapid strides in medical sciences and AI-driven integration of tools in existing healthcare models, one question often comes to the human mind: “Will the improvements in AI signal an end to clinical medicine and the clinicians?” For medical professionals, this debate is not merely philosophical; it has direct implications for patient safety, professional roles, ethics, health system design, medical education, and medical research. To attempt to answer this question, it is imperative to understand what is the status of AI at present, and where, hypothetically, AI would lead us in the coming years. Furthermore, it needs to be compared with the present-day human intelligence in medical practice, which involves not only analytical ability but also empathy, ethical judgment, and situational awareness. UNDERSTANDING ARTIFICIAL INTELLIGENCE: NARROW INTELLIGENCE VERSUS GENERAL INTELLIGENCE Current AI systems are forms of artificial narrow intelligence. These systems are designed to perform specific tasks, often at or above human level, but the drawback is that they lack general reasoning capability. Some commonplace examples include the deep learning algorithms for radiologic image interpretation (e.g., mammography, chest computed tomography [CT], and retinal imaging), the predictive models for sepsis, acute kidney injury, mortality risk in intensive care, and the natural language processing systems that structure clinical notes or assist in documentation. In sharp contrast, artificial general intelligence (AGI) refers to a hypothetical system capable of understanding, learning, and applying knowledge across domains in a manner comparable to or exceeding human cognitive abilities. AGI would be able to transfer learning between tasks, demonstrate genuine understanding, and autonomously define goals. In the present era, no true AGI exists. The contemporary AI of today’s world, including the large language models, operates by detecting statistical patterns in vast datasets rather than possessing consciousness or genuine understanding. This distinction is of critical import, when one evaluates the claims that AI will imminently surpass human intelligence. CURRENT CAPABILITIES OF ARTIFICIAL INTELLIGENCE IN MEDICINE AI has already achieved superhuman performance in specific, constrained medical tasks. The notable examples in this regard include the following. Medical imaging Deep convolutional neural networks have demonstrated diagnostic accuracy comparable to, and in some cases exceeding, expert radiologists in identifying conditions such as diabetic retinopathy in fundus photographs, pulmonary nodules in CT scans, and breast cancer in mammography. These systems excel in pattern recognition, consistency, and speed. They do not suffer from fatigue, cognitive bias, or workload-related performance degradation, which are human traits. Hence, in essence, these are the strong points of AI integration. Predictive analytics and risk stratification Machine learning models can analyze electronic health records to predict the risk of hospital readmission, likelihood of clinical deterioration in intensive care units, and adverse drug reactions. Such tools often outperform traditional statistical risk scores, particularly when dealing with high-dimensional data. Drug discovery and genomics AI has accelerated protein structure prediction, identification of novel drug targets, and interpretation of complex genomic and transcriptomic data. These achievements demonstrate that in data-heavy, computationally intensive domains, AI already exceeds unaided human capabilities. WHAT “SURPASSING HUMAN INTELLIGENCE” MEANS IN MEDICINE In the medical context, intelligence is multidimensional. It includes: Analytical intelligence: Data interpretation, diagnostics, and probabilistic reasoning Clinical intuition: Pattern recognition formed through experience Contextual judgment: Integrating social, cultural, ethical, and psychological factors Empathy and communication: Understanding patient values and emotions Ethical reasoning: Balancing benefits, risks, autonomy, and justice. While AI is rapidly approaching or exceeding human performance in analytical domains, it remains profoundly limited in areas requiring human consciousness, emotional intelligence, moral reasoning, and real-world contextual understanding. Thus, even if AI were to surpass humans in raw data processing, it would not equate to holistic superiority in medical intelligence. WILL ARTIFICIAL INTELLIGENCE ACHIEVE SUPERHUMAN GENERAL INTELLIGENCE SOON? It is generally believed that near-term development of true AGI is unlikely. The probability of AGI evolving within the next 10–20 years is low, and realistic timelines may extend to several decades. Key barriers include: Lack of true understanding: Current models manipulate symbols and patterns without comprehension Energy and resource constraints: The computational requirements of cutting-edge models are enormous Absence of consciousness and intentionality: No current system possesses self-awareness or intrinsic motivation Limited causal reasoning: AI struggles with reasoning about cause–effect relationships in complex, real-world environments. Therefore, while AI will become increasingly powerful, the emergence of an intelligence that broadly and autonomously surpasses human cognition across domains, including medicine, is unlikely in the near future. RISKS OF OVERESTIMATING ARTIFICIAL INTELLIGENCE IN HEALTH CARE A critical concern in medical AI is automation bias, where clinicians may over-rely on algorithmic outputs despite potential errors. This is a likely scenario, and one which needs to be tackled. Overestimating AI’s intelligence can lead to: Diagnostic complacency Reduced clinical vigilance Failure to recognize algorithmic bias Patient safety risks. It is important to realize that AI systems are only as good as the data they are trained on. Therefore, whatever the data have been fed on to evolve the algorithms, the same will be valid for that scenario only. Algorithms may need to be modified for ethnic and geographical variations and for different scenarios. Biases in training datasets can propagate health inequities, particularly for underrepresented population. In addition, many AI models function as “black boxes,” offering limited interpretability, which challenges traditional standards of medical accountability. Thus, assuming AI will “surpass” human intelligence may itself be harmful if it undermines critical human oversight. Therefore, it is of utmost importance to have human oversight on AI-generated decisions. POTENTIAL PATHWAYS TOWARD SUPERINTELLIGENT MEDICAL ARTIFICIAL INTELLIGENCE If AI were ever to surpass human intelligence in medicine, it would likely occur through incremental advances rather than a sudden leap. Possible developments include: Multimodal AI systems capable of integrating imaging, genomics, laboratory values, wearable data, and social determinants of health Continual learning systems that adapt to new evidence in real time Causal inference-capable models that understand disease mechanisms, not only correlations Embodied AI in robotics capable of physical examination and procedural tasks. Even in these advanced scenarios, the role of humans would likely shift rather than disappear. Clinicians may function more as supervisors, interpreters, ethicists, and patient advocates. ETHICAL AND LEGAL IMPLICATIONS FOR MEDICAL PRACTICE If AI approaches or exceeds certain aspects of human intelligence, several ethical and legal questions are bound to arise. These include, first, accountability. Who is responsible for AI-driven errors – developers, hospitals, or clinicians? Second, is the issue of informed consent. Should patients be explicitly informed when AI significantly influences decisions? Thirdly, will be the issue of Equity. It is to anybody’s guess if advanced AI will widen or reduce health disparities? Finally, shall be the issue of Professional identity. It will be interesting to note how the physician’s role will evolve in this particular era of advanced AI. Importantly, “medical ethics” frameworks, such as beneficence, nonmaleficence, autonomy, and justice, will need reinterpretation in an AI-augmented environment. THE LIKELY FUTURE: AUGMENTATION, NOT REPLACEMENT The most plausible future is not one where AI surpasses and replaces human intelligence, but one of collaborative intelligence because AI excels at data processing, pattern recognition, and consistency, while humans excel at empathy, ethical reasoning, creativity, and holistic judgment. In this model, the best outcomes will occur when clinicians and AI systems work together. CONCLUSION AI has already surpassed human performance in specific, narrow medical tasks, particularly those involving large-scale data processing and pattern recognition. However, surpassing human intelligence in a general, comprehensive sense, especially within the nuanced, ethical, and empathetic domain of medical practice, remains unlikely in the near future. For medical professionals, the central challenge is not preparing for replacement by superintelligent machines, but learning to effectively integrate powerful AI tools into clinical practice while preserving the humanistic core of medicine. The future of health care is best understood not as a competition between human and AI, but as a partnership that, when carefully governed, can enhance both safety and quality of care. Medical professionals are known to be resistant to change, but AI-driven health care and AI-integrated medical education are here to stay. The contemporary medical professional needs to adapt to the changing times of AI-incorporated healthcare systems and integrate these AI systems in his/her day-to-day practice to be able to perform better and stay relevant in the times to come. Financial support and sponsorship None. Conflicts of interest There are no conflicts of interest.