Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare

• Published in: Curēus (Palo Alto, CA) Vol. 15; no. 8; p. e43262
• Main Authors: Jeyaraman, Madhan, Balaji, Sangeetha, Jeyaraman, Naveen, Yadav, Sankalp
• Format: Journal Article
• Language: English
• Published: Palo Alto Springer Nature B.V 10.08.2023; Cureus
• Subjects: Algorithms; Artificial intelligence; Chatbots; Clinical decision making; Compliance; Confidentiality; Deep learning; Epidemiology/Public Health; Ethics; Healthcare Technology; Language; Medical coding; Medical equipment; Medical imaging; Natural language; Patients; Privacy; Public Health; R&D; Research & development; Trust; Well being
• ISSN: 2168-8184; 2168-8184
• DOI: 10.7759/cureus.43262

The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and decision-making. This transformative technology uses machine learning, natural language processing, and large language models (LLMs) to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated LLM, holds immense potential in medical practice, research, and education. However, as AI in healthcare gains momentum, it brings forth profound ethical challenges that demand careful consideration. This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality. Protecting patient privacy in data-driven healthcare is crucial, with potential implications for psychological well-being and data sharing. Strategies like homomorphic encryption (HE) and secure multiparty computation (SMPC) are vital to preserving confidentiality. Transparency and trustworthiness of AI systems are essential, particularly in high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity becomes a pressing concern as AI's complexity creates vulnerabilities for potential breaches. Determining responsibility in AI-driven outcomes raises important questions, with debates on AI's moral agency and human accountability. Shifting from data ownership to data stewardship enables responsible data management in compliance with regulations. Addressing bias in healthcare data is crucial to avoid AI-driven inequities. Biases present in data collection and algorithm development can perpetuate healthcare disparities. A public-health approach is advocated to address inequalities and promote diversity in AI research and the workforce. Maintaining data quality is imperative in AI applications, with convolutional neural networks showing promise in multi-input/mixed data models, offering a comprehensive patient perspective. In this ever-evolving landscape, it is imperative to adopt a multidimensional approach involving policymakers, developers, healthcare practitioners, and patients to mitigate ethical concerns. By understanding and addressing these challenges, we can harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.