Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts

Spine surgeons facing second opinions: a qualitative study

BACKGROUND CONTEXT

The social and technological mutation of our contemporary period disrupts the traditional dyad that prevails in the relationship between physicians and patients.

PURPOSE

The solicitation of a second opinion by the patient may potentially alter this dyad and degrade the mutual trust between the stakeholders concerned. The doctor-patient relationship has often been studied from the patient's perspective, but data are scarce from the spine surgeon's point of view.

STUDY DESIGN/SETTING

This qualitative study used the grounded theory approach, an inductive methodology emphasizing field data and rejecting predetermined assumptions.

PATIENT SAMPLE

We interviewed spine surgeons of different ages, experiences, and practice locations. We initially contacted 30 practitioners, but the final number (24 interviews; 11 orthopedists and 13 neurosurgeons) was determined by data saturation (the point at which no new topics appeared).

OUTCOME MEASURES

Themes and subthemes were analyzed using semistructured interviews until saturation was reached.

METHODS

Data were collected through individual interviews, independently analyzed thematically using specialized software, and triangulated by three researchers (an anthropologist, psychiatrist, and neurosurgeon).

RESULTS

Index surgeons were defined when their patients went for a second opinion and recourse surgeons were defined as surgeons who were asked for a second opinion. Data analysis identified five overarching themes based on recurring elements in the interviews: (1) analysis of the patient's motivations for seeking a second opinion; (2) impaired trust and disloyalty; (3) ego, authority, and surgeon image; (4) management of a consultation recourse (measurement and ethics); and (5) the second opinion as an avoidance strategy. Despite the inherent asymmetry in the doctor-patient relationship, surgeons and patients share two symmetrical continua according to their perspective (professional or consumerist), involving power and control on the one hand and loyalty and autonomy on the other. These shared elements can be found in index consultations (seeking high-level care/respecting trust/closing the loyalty gap/managing disengagement) and referral consultations (objective and independent advice/trusting of the index advice/avoiding negative and anxiety-provoking situations).

CONCLUSIONS

The second opinion often has a negative connotation with spine surgeons, who see it as a breach of loyalty and trust, without neglecting ego injury in their relationship with the patient. A paradigm shift would allow the second opinion to be perceived as a valuable resource that broadens the physician-patient relationship and optimizes the shared surgical decision-making process.

What Are Humans Doing in the Loop? Co-Reasoning and Practical Judgment When Using Machine Learning-Driven Decision Aids

Within the ethical debate on Machine Learning-driven decision support systems (ML_CDSS), notions such as "human in the loop" or "meaningful human control" are often cited as being necessary for ethical legitimacy. In addition, ethical principles usually serve as the major point of reference in ethical guidance documents, stating that conflicts between principles need to be weighed and balanced against each other. Starting from a neo-Kantian viewpoint inspired by Onora O'Neill, this article makes a concrete suggestion of how to interpret the role of the "human in the loop" and to overcome the perspective of rivaling ethical principles in the evaluation of AI in health care. We argue that patients should be perceived as "fellow workers" and epistemic partners in the interpretation of ML_CDSS outputs. We further highlight that a meaningful process of integrating (rather than weighing and balancing) ethical principles is most appropriate in the evaluation of medical AI.

Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging

OBJECTIVES

This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise.

METHODS

We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated.

RESULTS

The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance.

CONCLUSIONS

The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.

Assessing the Impact of AI Education on Hispanic Healthcare Professionals' Perceptions and Knowledge

This study investigates the awareness and perceptions of artificial intelligence (AI) among Hispanic healthcare-related professionals, focusing on integrating AI in healthcare. The study participants were recruited from an asynchronous course offered twice within a year at the University of Puerto Rico Medical Science Campus, titled "Artificial Intelligence and Machine Learning Applied to Health Disparities Research", which aimed to bridge the gaps in AI knowledge among participants. The participants were divided into Experimental (n = 32; data-illiterate) and Control (n = 18; data-literate) groups, and pre-test and post-test surveys were administered to assess knowledge and attitudes toward AI. Descriptive statistics, power analysis, and the Mann-Whitney U test were employed to determine the influence of the course on participants' comprehension and perspectives regarding AI. Results indicate significant improvements in knowledge and attitudes among participants, emphasizing the effectiveness of the course in enhancing understanding and fostering positive attitudes toward AI. Findings also reveal limited practical exposure to AI applications, highlighting the need for improved integration into education. This research highlights the significance of educating healthcare professionals about AI to enable its advantageous incorporation into healthcare procedures. The study provides valuable perspectives from a broad spectrum of healthcare workers, serving as a basis for future investigations and educational endeavors aimed at AI implementation in healthcare.

Physician's autonomy in the face of AI support: walking the ethical tightrope

The introduction of AI support tools raises questions about the normative orientation of medical practice and the need to rethink its basic concepts. One of these concepts that is central to the discussion is the physician’s autonomy and its appropriateness in the face of high-powered AI applications. In this essay, a differentiation of the physician’s autonomy is made on the basis of a conceptual analysis. It is argued that the physician’s decision-making autonomy is a purposeful autonomy. The physician’s decision-making autonomy is fundamentally anchored in the medical ethos for the purpose to promote the patient’s health and well-being and to prevent him or her from harm. It follows from this purposefulness that the physician’s autonomy is not to be protected for its own sake, but only insofar as it serves this end better than alternative means. We argue that today, given existing limitations of AI support tools, physicians still need physician’s decision-making autonomy. For the possibility of physicians to exercise decision-making autonomy in the face of AI support, we elaborate three conditions: (1) sufficient information about AI support and its statements, (2) sufficient competencies to integrate AI statements into clinical decision-making, and (3) a context of voluntariness that allows, in justified cases, deviations from AI support. If the physician should fulfill his or her moral obligation to promote the health and well-being of the patient, then the use of AI should be designed in such a way that it promotes or at least maintains the physician’s decision-making autonomy.

Artificial intelligence in pediatric surgery

Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.

Responsibility and decision-making authority in using clinical decision support systems: an empirical-ethical exploration of German prospective professionals' preferences and concerns

Machine learning-driven clinical decision support systems (ML-CDSSs) seem impressively promising for future routine and emergency care. However, reflection on their clinical implementation reveals a wide array of ethical challenges. The preferences, concerns and expectations of professional stakeholders remain largely unexplored. Empirical research, however, may help to clarify the conceptual debate and its aspects in terms of their relevance for clinical practice. This study explores, from an ethical point of view, future healthcare professionals' attitudes to potential changes of responsibility and decision-making authority when using ML-CDSS. Twenty-seven semistructured interviews were conducted with German medical students and nursing trainees. The data were analysed based on qualitative content analysis according to Kuckartz. Interviewees' reflections are presented under three themes the interviewees describe as closely related: (self-)attribution of responsibility, decision-making authority and need of (professional) experience. The results illustrate the conceptual interconnectedness of professional responsibility and its structural and epistemic preconditions to be able to fulfil clinicians' responsibility in a meaningful manner. The study also sheds light on the four relata of responsibility understood as a relational concept. The article closes with concrete suggestions for the ethically sound clinical implementation of ML-CDSS.

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.

Ebenen der Explizierbarkeit für medizinische künstliche Intelligenz: Was brauchen wir normativ und was können wir technisch erreichen?

Definition of the problem

The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI?

Arguments

We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example.

Conclusion

We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements.

Enabling Fairness in Healthcare Through Machine Learning

The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use of affirmative algorithms; that is, algorithms trained on diverse datasets that perform better for traditionally disadvantaged groups. Whilst such algorithmic decisions may be unfair, the fairness of algorithmic decisions is not the appropriate locus of moral evaluation. What matters is the fairness of final decisions, such as diagnoses, resulting from collaboration between clinicians and algorithms. We argue that affirmative algorithms can permissibly be deployed provided the resultant final decisions are fair.

"Many roads lead to Rome and the Artificial Intelligence only shows me one road": an interview study on physician attitudes regarding the implementation of computerised clinical decision support systems

Research regarding the drivers of acceptance of clinical decision support systems (CDSS) by physicians is still rather limited. The literature that does exist, however, tends to focus on problems regarding the user-friendliness of CDSS. We have performed a thematic analysis of 24 interviews with physicians concerning specific clinical case vignettes, in order to explore their underlying opinions and attitudes regarding the introduction of CDSS in clinical practice, to allow a more in-depth analysis of factors underlying (non-)acceptance of CDSS. We identified three general themes from the results. First, 'the perceived role of the AI', including items referring to the tasks that may properly be assigned to the CDSS according to the respondents. Second, 'the perceived role of the physician', referring to the aspects of clinical practice that were seen as being fundamentally 'human' or non-automatable. Third, 'concerns regarding AI', including items referring to more general issues that were raised by the respondents regarding the introduction of CDSS in general and/or in clinical medicine in particular. Apart from the overall concerns expressed by the respondents regarding user-friendliness, we will explain how our results indicate that our respondents were primarily occupied by distinguishing between parts of their job that should be automated and aspects that should be kept in human hands. We refer to this distinction as 'the division of clinical labor.' This division is not based on knowledge regarding AI or medicine, but rather on which parts of a physician's job were seen by the respondents as being central to who they are as physicians and as human beings. Often the respondents' view that certain core parts of their job ought to be shielded from automation was closely linked to claims concerning the uniqueness of medicine as a domain. Finally, although almost all respondents claimed that they highly value their final responsibility, a closer investigation of this concept suggests that their view of 'final responsibility' was not that demanding after all.