Virtual Doctor: The role, responsibilities, and counterparts of the general medical AI

Abstract

The apparition of multimodal solutions helped closing this breach by teaching AI solutions how to use multiple inputs to infer a given task. Elevating AI algorithms to a higher level by increasing robustness and accuracy. This approach also made models more human, by learning from multiple sources just as how clinicians do. However, such models are still far from being flexible due to mostly be constrained into one singular task, vastly differing from real clinicians who are in charge of broad fields with multiple tasks to perform. This niche is perfect for AI algorithms to grow. However, existing AI models lack flexibility to act similarly to doctors. Thus, some innovations are required to overcome this rigidity. One possible solution would be the emergence of Medical Artificial Intelligence (GMAI) seeks to evolve the current paradigm by aspiring to emulate more humanlike intelligence in medical applications. The emergence of these "Virtual Doctors" has the potential to significantly transform the healthcare paradigm, offering convenient healthcare solutions with just a few clicks at any time of the day. The focal point of this paper is to delineate the prospective applications of GMAI within the field of telemedicine, positioned as ‘teledoctors’, and to outline the existing technological gap between these innovative models and their conventional counterparts. More specifically, we envision GMAI as a ‘Virtual Doctor,’ functioning as an adaptable telemedicine system capable of addressing a broad array of inquiries and providing diagnoses for a diverse range of medical conditions by utilizing patient- or doctor-provided data inputs. While a general AI model for medical imaging may offer some benefits, it is vital to consider such a model’s potential drawbacks and limitations. It is crucial to balance the benefits of AI and the need for human expertise and oversight in medical imaging.

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