Joshua Hatherley
Datos Biográficos
| ID | 6703448 |
|---|---|
| NOMBRE | Joshua Hatherley |
| NOMBRES | Joshua |
| APELLIDO | Hatherley |
| FIRMA | HATHERLEY J |
| AFILIACIONES | Monash University |
| ORCID | 0000-0002-8581-9669 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 8 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 8 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2020 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 0 |
In Defense of Post Hoc Explanations in Medical AI
Since the early days of the explainable artificial intelligence movement, post hoc explanations have been praised for their potential to improve user understanding, promote trust, and reduce patient-safety risks in black box medical AI systems. Recently, however, critics have argued that the benefits of post hoc explanations are greatly exaggerated since they merely approximate, rather than replicate, the actual reasoning processes that black box…
Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this ‘the disclosure thesis.’ Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the material…
A moving target in AI-assisted decision-making
Machine learning (ML) systems are vulnerable to performance decline over time due to dataset shift. To address this problem, experts often suggest that ML systems should be regularly updated to ensure ongoing performance stability. Some scholarly literature has begun to address the epistemic and ethical challenges associated with different updating methodologies. Thus far, however, little attention has been paid to the impact of model updating on…
The Virtues of Interpretable Medical AI
Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretabil…
The Virtues of Interpretable Medical Artificial Intelligence
Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretabil…
Medical assistance in dying for the psychiatrically ill reply to Buturovic
In a recent Response published in the Journal of Medical Ethics ,1 Buturovic provides two criticisms of my argument in ‘Is the exclusion of psychiatric patients from access to physician-assisted suicide discriminatory?’2 First, Buturovic argues that my argument effectively ‘erases the distinction between healthy adults and patients (whether somatic or psychiatric) essentially implying that PAS [physician-assisted suicide] should be available to a…
Limits of trust in medical AI
Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in cli…
High Hopes for “Deep Medicine”? AI, Economics, and the Future of Care
In the much‐celebrated book Deep Medicine, Eric Topol argues that the development of artificial intelligence for health care will lead to a dramatic shift in the culture and practice of medicine. In the next several decades, he suggests, AI will become sophisticated enough that many of the everyday tasks of physicians could be delegated to it. Topol is perhaps the most articulate advocate of the benefits of AI in medicine, but he is hardly alone …
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Limits of trust in medical AI
Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in cli…
High Hopes for “Deep Medicine”? AI, Economics, and the Future of Care
In the much‐celebrated book Deep Medicine, Eric Topol argues that the development of artificial intelligence for health care will lead to a dramatic shift in the culture and practice of medicine. In the next several decades, he suggests, AI will become sophisticated enough that many of the everyday tasks of physicians could be delegated to it. Topol is perhaps the most articulate advocate of the benefits of AI in medicine, but he is hardly alone …
Medical assistance in dying for the psychiatrically ill reply to Buturovic
In a recent Response published in the Journal of Medical Ethics ,1 Buturovic provides two criticisms of my argument in ‘Is the exclusion of psychiatric patients from access to physician-assisted suicide discriminatory?’2 First, Buturovic argues that my argument effectively ‘erases the distinction between healthy adults and patients (whether somatic or psychiatric) essentially implying that PAS [physician-assisted suicide] should be available to a…
The Virtues of Interpretable Medical Artificial Intelligence
Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretabil…
The Virtues of Interpretable Medical AI
Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretabil…
Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this ‘the disclosure thesis.’ Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the material…
A moving target in AI-assisted decision-making
Machine learning (ML) systems are vulnerable to performance decline over time due to dataset shift. To address this problem, experts often suggest that ML systems should be regularly updated to ensure ongoing performance stability. Some scholarly literature has begun to address the epistemic and ethical challenges associated with different updating methodologies. Thus far, however, little attention has been paid to the impact of model updating on…
In Defense of Post Hoc Explanations in Medical AI
Since the early days of the explainable artificial intelligence movement, post hoc explanations have been praised for their potential to improve user understanding, promote trust, and reduce patient-safety risks in black box medical AI systems. Recently, however, critics have argued that the benefits of post hoc explanations are greatly exaggerated since they merely approximate, rather than replicate, the actual reasoning processes that black box…
Artificial Intelligence in Healthcare and Education (6 obras) · Medicine (6 obras) · Psychology (5 obras) · Artificial Intelligence (4 obras) · Computer Science (4 obras) · Explainable Artificial Intelligence (XAI (4 obras) · Harm (3 obras) · Law (3 obras) · Machine learning (3 obras) · Machine Learning in Healthcare (3 obras)