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Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT

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Abstract

Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Christ (Deemed to be University), Kanminike, Bangalore, Karnataka, 560074, India

    R. Venkataswamy & Varaprasad Janamala

  2. Department of Computer Science and Engineering, Christ (Deemed to be University), Kanminike, Bangalore, Karnataka, 560074, India

    Ravidranath Chowdary Cherukuri

Authors
  1. R. Venkataswamy
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  2. Varaprasad Janamala
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  3. Ravidranath Chowdary Cherukuri
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Corresponding author

Correspondence to R. Venkataswamy.

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The authors declare that they have no conflict of interest.

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Associate Editor Stefan M. Duma oversaw review of this article.

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Venkataswamy, R., Janamala, V. & Cherukuri, R.C. Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT. Ann Biomed Eng 52, 738–740 (2024). https://doi.org/10.1007/s10439-023-03316-9

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  • DOI: https://doi.org/10.1007/s10439-023-03316-9

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