Against the iDoctor: why artificial intelligence should not replace physician judgment

Perceptions of primary care patients on the use of electronic clinical decision support tools to facilitate health care: A systematic review

OBJECTIVE

Electronic clinical decision support tools (eCDSTs) are interventions designed to facilitate clinical decision-making using targeted medical knowledge and patient information. While eCDSTs have been demonstrated to improve quality of care, there is a paucity of research relating to the acceptability of eCDSTs in primary care from the patients' perspective. This study aims to summarize current evidence relating to primary care patients' perceptions and experiences on the use of eCDSTs by their clinician to provide care.

METHODS

Four databases (Medline, Embase, CINAHL and Cochrane Library) were searched for qualitative and quantitative studies with outcomes relating to patients' perceptions of the use of clinician-facing or shared-eCDSTs. Data extraction and critical appraisal using the Johanna Briggs Institute Critical Appraisal checklists were carried out independently by reviewers. Qualitative and quantitative outcomes were synthesized independently. We used Richardson et al. 'Patient Evaluation of Artificial Intelligence (AI) in Healthcare' framework for qualitative analysis.

FINDINGS

20 papers were included for synthesis. eCDSTs were generally well-regarded by patients. The key facilitators for use were promoting informed decision-making, prompting discussions, aiding clinical decision-making, and enabling information sharing. Key barriers for use were lack of holistic care, 'medicalized' language, and confidentiality concerns.

CONCLUSION

Our study identified important aspects to consider in the development of future eCDSTs. Patients were generally positive regarding the use of eCDSTs; however, patient's perspectives should be included from the conception of new eCDSTs to ensure recommendations align with the needs of patients and clinicians.

PRACTICE IMPLICATIONS

The study results contribute to ensuring the acceptability of eCDSTs for patients and their unique needs. Encouragement is given for future development to adopt and build upon these findings. Additional research focusing on patients' perceptions of using eCDSTs for specific health conditions is deemed necessary.

Care Delivery Interventions for Individuals with Cancer: A Literature Review and Focus on Gastrointestinal Malignancies

BACKGROUND

Gastrointestinal malignancies represent a particularly challenging condition, often requiring a multidisciplinary approach to management in order to meet the unique needs of these individuals and their caregivers.

PURPOSE

In this literature review, we sought to describe care delivery interventions that strive to improve the quality of life and care for patients with a focus on gastrointestinal malignancies.

CONCLUSION

We highlight patient-centered care delivery interventions, including patient-reported outcomes, hospital-at-home interventions, and other models of care for individuals with cancer. By demonstrating the relevance and utility of these different care models for patients with gastrointestinal malignancies, we hope to highlight the importance of developing and testing new interventions to address the unique needs of this population.

New Horizons in artificial intelligence in the healthcare of older people

Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

Evaluation of acceptance, attitude, and knowledge towards artificial intelligence and its application from the point of view of physicians and nurses: A provincial survey study in Iran: A cross-sectional descriptive-analytical study

Background and Aims

The prospect of using artificial intelligence (AI) in healthcare is bright and promising, and its use can have a significant impact on cost reduction and decrease the possibility of error and negligence among healthcare workers. This study aims to investigate the level of knowledge, attitude, and acceptance among Iranian physicians and nurses.

Methods

This cross-sectional descriptive-analytical study was conducted in eight public university hospitals located in Tehran on 400 physicians and nurses. To conduct the study, convenient sampling was used with the help of researcher-made questionnaires. Statistical analysis was done by SPSS 21 The mean and standard deviation and Chi-square and Fisher's exact tests were used.

Results

In this study, the level of knowledge among the research subjects was average (14.66 ± 4.53), the level of their attitude toward AI was relatively favorable (47.81 ± 6.74), and their level of acceptance of AI was average (103.19 ± 13.70). Moreover, from the participant's perspective, AI in medicine is most widely used in increasing the accuracy of diagnostic tests (86.5%), identifying drug interactions (82.75%), and helping to analyze medical tests and imaging (80%). There was a statistically significant relationship between the variable of acceptance of AI and the participant's level of education (p = 0.028), participation in an AI training course (p = 0.022), and the hospital department where they worked (p < 0.001).

Conclusion

In this study, both the knowledge and the acceptance of the participants towards AI were proved to be at an average level and the attitude towards AI was relatively favorable, which is in contrast with the very rapid and inevitable expansion of AI. Although our participants were aware of the growing use of AI in medicine, they had a cautious attitude toward this.

Innovation in healthcare: leadership perceptions about the innovation characteristics of artificial intelligence-a qualitative interview study with healthcare leaders in Sweden

BACKGROUND

Despite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.

METHODS

The study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.

RESULTS

Participants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.

CONCLUSION

Healthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI's opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare.

The Moral Difference between Faces & FaceTime

Although the technology for telemedicine existed before the Covid-19 pandemic, the need to provide medical services while minimizing the risk of contagion has encouraged its more widespread use. I argue that, although telemedicine can be useful in certain situations, physicians should not consider it an adequate substitute for the office visit. I first provide a narrative account of the experience of telemedicine. I then draw on philosophical critiques of technology to examine how telemedicine has epistemic and ethical effects that make some of the goods of medicine unavailable. Telemedicine rules out an embodied encounter between physician and patient, in which the sense of touch has special importance. The individualized attention facilitated by the in-person visit may better sustain a caring physician-patient relationship. Physicians should criticize attempts by administrators, insurers, or other parties to incentivize the wholesale replacement of traditional office visits with telemedicine.

Facing Progress with Pragmatism: Telemedicine and Family Medicine

The singular expertise of family physicians is the ability to manage complexity with pragmatism, both clinically and ethically. Telemedicine raises multiple questions about the nature of the patient-physician relationship as manifested in clinical encounters. Some of these questions are concerning, underscoring the need to assess whether medical care is better with this new technology-or if it is just different or maybe even worse. It seems clear, however, that, regardless of its limitations, telemedicine is here to stay. The pragmatic complex ethical question, then, is how all of us together-both medical professionals and society at large-will manage it.

Artificial intelligence in orthopedics: A qualitative exploration of the surgeon perspective

Artificial intelligence (AI) is currently integrated into many medical services. AI is utilized in many aspects of orthopedic surgery. The scope ranges from diagnosis to complex surgery. To evaluate the perceptions, attitudes, and interests of Sudanese orthopedic surgeons regarding the different applications of AI in orthopedic surgery. This qualitative questionnaire-based study was conducted through an anonymous electronic survey using Google Forms distributed among Sudanese orthopedic surgeons. The questionnaire entailed 4 sections. The first section included the participants' demographic data. The remaining 3 sections included questions for the assessment of the perception, attitude, and interest of surgeons toward (AI). The validity and reliability of the questionnaire were tested and piloted before the final dissemination. One hundred twenty-nine surgeons responded to the questionnaires. Most respondents needed to be more aware of the basic concepts of AI. However, most respondents were aware of its use in spinal and joint replacement surgeries. Most respondents had doubts regarding the safety of (AI). However, they were highly interested in utilizing (AI) in many orthopedic surgical aspects. Orthopedic surgery is a rapidly evolving branch of surgery that involves adoption of new technologies. Therefore, orthopedic surgeons should be encouraged to enroll in research activities to generate more studies and reviews to assess the usefulness and safety of emerging technologies.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

The medical profession transformed by artificial intelligence: Qualitative study

BACKGROUND

Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear.

OBJECTIVE

We analyzed physicians' and AI experts' stances towards AI-induced changes. This concerned (1) physicians' tasks, (2) job replacement risk, and (3) implications for the ways of working, including human-AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns.

METHODS

We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed.

RESULTS

Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient-physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human-AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization.

CONCLUSIONS

The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.

Informatics in Undergraduate Medical Education: Analysis of Competency Frameworks and Practices Across North America

BACKGROUND

With the advent of competency-based medical education, as well as Canadian efforts to include clinical informatics within undergraduate medical education, competency frameworks in the United States have not emphasized the skills associated with clinical informatics pertinent to the broader practice of medicine.

OBJECTIVE

By examining the competency frameworks with which undergraduate medical education in clinical informatics has been developed in Canada and the United States, we hypothesized that there is a gap: the lack of a unified competency set and frame for clinical informatics education across North America.

METHODS

We performed directional competency mapping between Canadian and American graduate clinical informatics competencies and general graduate medical education competencies. Directional competency mapping was performed between Canadian roles and American common program requirements using keyword matching at the subcompetency and enabling competency levels. In addition, for general graduate medical education competencies, the Physician Competency Reference Set developed for the Liaison Committee on Medical Education was used as a direct means of computing the ontological overlap between competency frameworks.

RESULTS

Upon mapping Canadian roles to American competencies via both undergraduate and graduate medical education competency frameworks, the difference in focus between the 2 countries can be thematically described as a difference between the concepts of clinical and management reasoning.

CONCLUSIONS

We suggest that the development or deployment of informatics competencies in undergraduate medical education should focus on 3 items: the teaching of diagnostic reasoning, such that the information tasks that comprise both clinical and management reasoning can be discussed; precision medical education, where informatics can provide for more fine-grained evaluation; and assessment methods to support traditional pedagogical efforts (both at the bedside and beyond). Assessment using cases or structured assessments (eg, Objective Structured Clinical Examinations) would help students draw parallels between clinical informatics and fundamental clinical subjects and would better emphasize the cognitive techniques taught through informatics.

An integration engineering framework for machine learning in healthcare

Background and Objectives

Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare.

Methods

Applied systems engineering, software engineering and health care Machine Learning software development practices were reviewed and critically appraised to establish an understanding of limitations and challenges within these domains. Principles of systems engineering were used to develop solutions to address the identified problems. The framework was then harmonized with the Machine Learning software development process to create a systems engineering-based Machine Learning software development approach in the healthcare domain.

Results

We present an integration framework for healthcare Artificial Intelligence that considers the entirety of this system of systems. Our proposed framework utilizes a combined software and integration engineering approach and consists of four phases: (1) Inception, (2) Preparation, (3) Development, and (4) Integration. During each phase, we present specific elements for consideration in each of the three domains of integration: The Human, The Technical System, and The Environment. There are also elements that are considered in the interactions between these domains.

Conclusion

Clinical models are technical systems that need to be integrated into the existing system of systems in health care. A systems engineering approach to integration ensures appropriate elements are considered at each stage of model design to facilitate model integration. Our proposed framework is based on principles of systems engineering and can serve as a guide for model development, increasing the likelihood of successful Machine Learning translation and integration.

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.

Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy

Artificial intelligence (AI) has seen tremendous growth over the past decade and stands to disrupts the medical industry. In medicine, this has been applied in medical imaging and other digitised medical disciplines, but in more traditional fields like medical physics, the adoption of AI is still at an early stage. Though AI is anticipated to be better than human in certain tasks, with the rapid growth of AI, there is increasing concerns for its usage. The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Topics on AI for image acquisition, image segmentation, treatment delivery, quality assurance and outcome prediction will be explored as well as the interaction between human and AI. This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.

Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine

The rapid adoption and implementation of artificial intelligence in medicine creates an ontologically distinct situation from prior care models. There are both potential advantages and disadvantages with such technology in advancing the interests of patients, with resultant ontological and epistemic concerns for physicians and patients relating to the instatiation of AI as a dependent, semi- or fully-autonomous agent in the encounter. The concept of libertarian paternalism potentially exercised by AI (and those who control it) has created challenges to conventional assessments of patient and physician autonomy. The unclear legal relationship between AI and its users cannot be settled presently, an progress in AI and its implementation in patient care will necessitate an iterative discourse to preserve humanitarian concerns in future models of care. This paper proposes that physicians should neither uncritically accept nor unreasonably resist developments in AI but must actively engage and contribute to the discourse, since AI will affect their roles and the nature of their work. One's moral imaginative capacity must be engaged in the questions of beneficence, autonomy, and justice of AI and whether its integration in healthcare has the potential to augment or interfere with the ends of medical practice.

Machine learning in primary care: potential to improve public health

It is estimated that missed opportunities for diagnosis occur in 1 in 20 primary care appointments. This is not only detrimental to individual patients, but also to the healthcare system as health outcomes are affected and healthcare expenditure inevitably increases. There are many potential solutions to limit the number of missed opportunities for diagnosis and management, one of which is through the use of artificial intelligence. Artificial intelligence and machine learning research and capabilities have exponentially grown in the past decades, with their applications in medicine showing great promise. As such, this review aims to discuss the possible uses of machine learning in primary care to maximise the quality of care provided.

Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study

Objectives: The medical community is in agreement that artificial intelligence (AI) will have a radical impact on patient care in the near future. The purpose of this study is to assess the awareness of AI technologies among health professionals and to investigate their perceptions toward AI applications in medicine. Design: A web-based Google Forms survey was distributed via the Royal Free London NHS Foundation Trust e-newsletter. Setting: Only staff working at the NHS Foundation Trust received an invitation to complete the online questionnaire. Participants: 98 healthcare professionals out of 7,538 (response rate 1.3%; CI 95%; margin of error 9.64%) completed the survey, including medical doctors, nurses, therapists, managers, and others. Primary outcome: To investigate the prior knowledge of health professionals on the subject of AI as well as their attitudes and worries about its current and future applications. Results: 64% of respondents reported never coming across applications of AI in their work and 87% did not know the difference between machine learning and deep learning, although 50% knew at least one of the two terms. Furthermore, only 5% stated using speech recognition or transcription applications on a daily basis, while 63% never utilize them. 80% of participants believed there may be serious privacy issues associated with the use of AI and 40% considered AI to be potentially even more dangerous than nuclear weapons. However, 79% also believed AI could be useful or extremely useful in their field of work and only 10% were worried AI will replace them at their job. Conclusions: Despite agreeing on the usefulness of AI in the medical field, most health professionals lack a full understanding of the principles of AI and are worried about potential consequences of its widespread use in clinical practice. The cooperation of healthcare workers is crucial for the integration of AI into clinical practice and without it the NHS may miss out on an exceptionally rewarding opportunity. This highlights the need for better education and clear regulatory frameworks.

Artificial Intelligence in Radiotherapy: A Philosophical Perspective

The increasing uptake of machine learning solutions for segmentation and planning leaves no doubt that artificial intelligence (AI) will soon be providing input into a range of radiotherapy procedures. Although this promises to deliver increased speed and accuracy, the future role of AI in relation to radiotherapy should be thought through carefully. There is currently a gap between published developments and widespread adoption, which provides some space to prepare the workforce and to consider the implications on practice. It is rare to find philosophical input into a medical journal, but the advent of AI makes this perspective increasingly important. Philosophical insight can help explore the potential impact of AI, in particular, on human creativity and oversight. Without this perspective, we run the risk of focusing solely on the immediate logistical impact on patients and departments. This commentary identifies three key aspects of radiotherapy that the authors feel would suffer most under AI control: creativity, innovation, and patient safety, which all demand uniquely human attributes. The article also provides insight from a philosophical perspective with regard to human consciousness, ethics, and empathy. Philosophically we should, perhaps, retain ethical concerns about the widening role of AI in radiotherapy beyond simple quantitative interpretation and image processing. As developments continue, we have time to determine how our roles will evolve and to establish a framework for ensuring appropriate human input into patient care. Most importantly, we must start to embed a philosophical approach to adoption of AI technology from the outset if we are to prepare ourselves for the challenge that lies ahead.