Possibilities and Pitfalls of Generative Pre-Trained Transformers in Healthcare

Abstract

For its potential use in healthcare, Generative Pre-trained Transformers (GPT) and comparable models have attracted a lot of attention. These models present opportunities for therapeutic decision assistance, effective recordkeeping, natural language interactions, and patient education. Their application in healthcare, however, also has some drawbacks and difficulties that need to be properly handled.. The applications of GPT models in healthcare are incredibly broad. Through natural language interactions, they can produce patient education materials, offer decision help to healthcare professionals, and enhance user experience. GPT models offer the potential to improve tele-medicine projects, healthcare process optimization, and patient engagement. They can also help in literature reviews, information retrieval, and medical research, which enable researchers and healthcare practitioners to stay current with evidence-based practices. When applying GPT models in healthcare, a number of issues must be taken into account. These include the limitations of medical knowledge, ethical issues, potential biases and informational errors, legal and regulatory compliance, and the difficulty of having limited contextual awareness. GPT models should be used to support human decision-making rather than to replace medical experts. Critical issues that must be taken into account include patient confidentiality, data security, and the ethical usage of GPT models. To establish credibility and validate the GPT models' outputs in healthcare contexts, improvements to their interpretability and explain ability are required. To guarantee the application and efficacy of GPT models in healthcare, domain-specific adaption and clinical validation are crucial research fields. To properly handle the opportunities and drawbacks of GPT models, collaboration between researchers, healthcare professionals, and policymakers is crucial. The potential for revolutionizing healthcare delivery is enormous with pre-trained Transformers. But the difficulties and potential traps they pose must be carefully considered. GPT models can be ethically implemented and help to improve healthcare outcomes by resolving ethical issues, guaranteeing data privacy and security, and proving their efficacy in clinical situations. For GPT models to be used in healthcare to their fullest potential and to reduce the hazards involved, further research and collaboration are required.

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