Issues in Radiology
Medical malpractice occurs when a health care professional neglects to deliver an apposite cure, gives a substandard treatment, or omits to take an appropriate step causing injury, harm, or even fatality of the patient. This may result in a lawsuit, but it may take time to resolve. This is because legal consultations must be held between the plaintiff attorney and plaintiff to determine the case’s merit. Once the plaintiff attorney is convinced that there is a solid ground for lawsuits, they prepare, which is then served to the defendant (Funaki & Brian 61). After serving this summary to the defendant, a lawsuit is filed if the defendant disagrees with the proposed settlement. However, before filing a lawsuit, a formal pretrial, deposition, is held involving both attorneys and the plaintiff as well as a court reporter. In most cases, this recorded evidence is used in a court of law, and the requirements are that the subsequent evidence should not vary from the recorded one.
To enhance medical efficacy and prevent lawsuits, artificial intelligence has been created and incorporated into medical devices, especially radiology. It is worth knowing that artificial intelligence is a subdivision of computer science devoted to creating systems that undertake tasks that involve human astuteness (Pesapane et al, 745). This intelligence has made it possible to deal with a large amount of data and minimizing errors. In particular, it has enabled medics to interpret medical images like the images acquired from Magnetic Resonance Imaging, computed tomography, among others. However, there various challenges associated with artificial intelligence when it comes to radiology. The main one includes; mirroring human biasness in decision-making, and more so, some devices may be designed to perform unethical practices. This has prompted policy initiatives to design with the predominant approach from both the US and the EU. The EU approach requires that the manufacturers have to produce medical devices suitable for their intended purpose. More so, the data protection by the General Data Protection Regulation emphasizes opt-in data use and processing (Pesapane et al, 749). In the US approach, medical devices definition has been revised to include any device used to mitigate, cure, prevent, and treat disease (Pesapane et al, 748). Additionally, any medical device that qualifies in this definition is regulated by the FDA, whereas the US’s data protection approach is mainly an opt-out method.
However, a survey conducted in the US revealed more that needs to be done, especially in the radiology field. Though radiology makes up almost 3.6% of the total medical practitioners in the US, a study reveals that it is ranked between positions six to eight in medicolegal claims (Imanzadeh et al, 138). A Qualtrics online survey administered to alumni, trainees, and attendees with special attention to the level of training, gender, and experience and practice setup further revealed that radiologists were well aware that they were at a higher possibility of being named in a lawsuit. More so, it revealed that there is still limited knowledge among radiologists about medicolegal topics, and more so, the respondents conveyed their will to acquire additional training(Imanzadeh et al, 142). The inspection also reviewed the addition of medicolegal subjects into non-interpretive skills that will be received with gratitude.
Works cited
Funaki, Brian. “Medical malpractice issues related to interventional radiology complications.” Seminars in interventional radiology. Vol. 32. No. 1. Thieme Medical Publishers, (2015): 61-64
Imanzadeh, Amir, Sarvenaz Pourjabbar, and Jonathan Mezrich. “Medicolegal training in radiology; an overlooked component of the non-interpretive skills curriculum.” Clinical Imaging (2020). 138-142
Pesapane, Filippo, et al. “Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States.” Insights into imaging 9.5 (2018): 745- 753.