Background: With the availability of extensive health data, artificial intelligence has an inordinate capability to expedite medical explorations and revamp healthcare.Artificial intelligence is set to reform the practice of medicine soon. Despite the mammoth advantages of artificial intelligence in the medical field, there exists inconsistency in the ethical and legal framework for the application of AI in healthcare. Although research has been conducted by various medical disciplines investigating the ethical implications of artificial intelligence in the healthcare setting, the literature lacks a holistic approach.
Objective: The purpose of this review is to ascertain the ethical concerns of AI applications in healthcare, to identify the knowledge gaps and provide recommendations for an ethical and legal framework.
Methodology: Electronic databases Pub Med and Google Scholar were extensively searched based on the search strategy pertaining to the purpose of this review. Further screening of the included articles was done on the grounds of the inclusion and exclusion criteria.
Results: The search yielded a total of 1238 articles, out of which 16 articles were identified to be eligible for this review. The selection was strictly based on the inclusion and exclusion criteria mentioned in the manuscript.
Conclusion: Artificial intelligence (AI) is an exceedingly puissant technology, with the prospect of advancing medical practice in the years to come. Nevertheless, AI brings with it a colossally abundant number of ethical and legal problems associated with its application in healthcare. There are manifold stakeholders in the legal and ethical issues revolving around AI and medicine. Thus, a multifaceted approach involving policymakers, developers, healthcare providers and patients is crucial to arrive at a feasible solution for mitigating the legal and ethical problems pertaining to AI in healthcare.
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http://dx.doi.org/10.3390/jpm12111914 | DOI Listing |
Sci Data
December 2024
Department of Pathology and Laboratory Medicine, Alpert Medical School, Brown University, Providence, RI, 02912, USA.
In the past several years, a few cervical Pap smear datasets have been published for use in clinical training. However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conventional Pap smear method. Multicellular liquid Pap image datasets are a more accurate reflection of current cervical screening techniques.
View Article and Find Full Text PDFJ Neuroeng Rehabil
December 2024
Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
Wearable robots are often powered by elastic actuators, which can mimic the intrinsic compliance observed in human joints, contributing to safe and seamless interaction. However, due to their increased complexity, when compared to direct drives, elastic actuators are susceptible to faults, which pose significant challenges, potentially compromising user experience and safety during interaction. In this article, we developed a fault-tolerant control strategy for torque assistance in a knee exoskeleton and investigated user experience during a walking task while emulating faults.
View Article and Find Full Text PDFBMC Med Educ
December 2024
Department of Orthopedics, Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab, 151203, India.
Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory.
View Article and Find Full Text PDFBMC Res Notes
December 2024
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
This dataset contains demographic, morphological and pathological data, endoscopic images and videos of 191 patients with colorectal polyps. Morphological data is included based on the latest international gastroenterology classification references such as Paris, Pit and JNET classification. Pathological data includes the diagnosis of the polyps including Tubular, Villous, Tubulovillous, Hyperplastic, Serrated, Inflammatory and Adenocarcinoma with Dysplasia Grade & Differentiation.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
December 2024
Klinikum Stuttgart, Stuttgart Cancer Center - Tumorzentrum Eva Mayr-Stihl DE, Kriegsbergstraße 60, Stuttgart, D-70174, Germany.
Background: Medical narratives are fundamental to the correct identification of a patient's health condition. This is not only because it describes the patient's situation. It also contains relevant information about the patient's context and health state evolution.
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