Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

Naik, Nithesh; Hameed, B. M. Zeeshan; Shetty, Dasharathraj K; Swain, Dishant; Shah, Milap; Paul, Rahul; Aggarwal, Kaivalya; Ibrahim, Sufyan; Patil, Vathsala; Smriti, Komal; Shetty, Suyog; Prasad, Bhavan; Chłosta, P.; Somani, Bhaskar

doi:10.3389/fsurg.2022.862322

Cited by 300 publications

(190 citation statements)

References 38 publications

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“…Since technologists are not obligated by law to be held accountable for their actions to the same degree as doctors, the question of whether, and if yes how much, they should take responsibility for the AI system they created, remains. An effort on clear guidelines for the legal and ethical issues concerning AI should be made [ 85 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

et al. 2022

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Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

et al. 2022

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“…They mention that transparency must be created when working with such algorithms. Furthermore, weaknesses such as cyberattacks and privacy invasion should not be ignored if you want to advance this field and research (21).…”

Section: Discussionmentioning

confidence: 99%

Developing and validating a multivariable prediction model for predicting costs of colon surgery

Taha

Enodien

Ochs

et al. 2022

Preprint

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Hospitals are faced with a burden to predict and therefore to calculate and also to manage various cost factors in regard to the patients and their treatments. Especially the increased costs which occur if a patient suffers from an anastomotic insufficiency. This can lead to an unavoidable deficit in the final revenue of these medical centers. This study aims to determine whether machine learning (ML) algorithms can predict the cost factors based on patients with and without colon anastomosis after colorectal surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool which can be helpful for hospitals to manage their costs, which can end up in operating more cost effectively. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a % MAPE result of 18 to 25.6 the prediction if our model showed decent results in order to forecast the costs in regard to various diagnosed factors and surgery approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with and without anastomotic leakage to minimize unnecessary costs for the hospitals.

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“…Given that results from AI tools may have a far-reaching impact on the life of a patient, there are important legal challenges to be considered for their safer translation into clinical practice [207]. An equitable liability scheme needs to be designed to define the roles of AI tool developers, physicians, and healthcare institutions.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

The state of the art for artificial intelligence in lung digital pathology

Viswanathan

Toro

Corredor

et al. 2022

The Journal of Pathology

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Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)‐based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand‐crafted and deep learning‐based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID‐19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD‐L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP‐based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

Cited by 300 publications

References 38 publications

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

Developing and validating a multivariable prediction model for predicting costs of colon surgery

The state of the art for artificial intelligence in lung digital pathology

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