The integration of Artificial Intelligence (AI) into healthcare has revolutionized various aspects of clinical practice, including the management of medical malpractice disputes. AI-driven technologies, particularly machine learning (ML) and natural language processing (NLP), enable the automated analysis of electronic health records (EHRs) and other medical documentation, improving the efficiency, accuracy, and transparency of malpractice investigations. By systematically identifying inconsistencies, detecting patterns of errors, and evaluating compliance with clinical guidelines, AI systems offer valuable insights into potential negligence claims. This study examines the impact of AI on medical record management in malpractice disputes, addressing its role in mitigating human biases, enhancing forensic assessments, and supporting legal decision-making. AI-powered algorithms facilitate objective analysis by cross-referencing vast datasets of patient histories, diagnostic reports, and treatment protocols, thus strengthening the evidentiary basis for malpractice claims. However, despite its advantages, the use of AI in forensic and legal medicine raises significant ethical and legal concerns, including issues of accountability, data privacy, and algorithmic bias. Questions regarding liability in AI-assisted medical decision-making and the potential risk of over-reliance on automated assessments must be critically addressed. To maximize AI's benefits while minimizing risks, robust regulatory frameworks, interdisciplinary collaboration, and ethical oversight are essential. Ensuring transparency in AI-driven decision-making and safeguarding patient rights will be crucial in fostering trust in these technologies. The findings suggest that AI-assisted medical record analysis can significantly enhance dispute resolution processes by providing standardized, data-driven evaluations of malpractice claims, ultimately contributing to more equitable and efficient healthcare litigation.
The role of artificial intelligence in analyzing clinical malpractice disputes through medical record management
Esposito, Massimiliano;Chisari, Mario
2025-01-01
Abstract
The integration of Artificial Intelligence (AI) into healthcare has revolutionized various aspects of clinical practice, including the management of medical malpractice disputes. AI-driven technologies, particularly machine learning (ML) and natural language processing (NLP), enable the automated analysis of electronic health records (EHRs) and other medical documentation, improving the efficiency, accuracy, and transparency of malpractice investigations. By systematically identifying inconsistencies, detecting patterns of errors, and evaluating compliance with clinical guidelines, AI systems offer valuable insights into potential negligence claims. This study examines the impact of AI on medical record management in malpractice disputes, addressing its role in mitigating human biases, enhancing forensic assessments, and supporting legal decision-making. AI-powered algorithms facilitate objective analysis by cross-referencing vast datasets of patient histories, diagnostic reports, and treatment protocols, thus strengthening the evidentiary basis for malpractice claims. However, despite its advantages, the use of AI in forensic and legal medicine raises significant ethical and legal concerns, including issues of accountability, data privacy, and algorithmic bias. Questions regarding liability in AI-assisted medical decision-making and the potential risk of over-reliance on automated assessments must be critically addressed. To maximize AI's benefits while minimizing risks, robust regulatory frameworks, interdisciplinary collaboration, and ethical oversight are essential. Ensuring transparency in AI-driven decision-making and safeguarding patient rights will be crucial in fostering trust in these technologies. The findings suggest that AI-assisted medical record analysis can significantly enhance dispute resolution processes by providing standardized, data-driven evaluations of malpractice claims, ultimately contributing to more equitable and efficient healthcare litigation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
