Artificial intelligence in healthcare: why not apply the medico-legal method starting with the Collingridge dilemma?

Technology has greatly influenced and radically changed human life, from communication to creativity and from productivity to entertainment. The authors, starting from considerations concerning the implementation of new technologies with a strong impact on people's everyday lives, take up Collingridge's dilemma and relate it to the application of AI in healthcare. Collingridge's dilemma is an ethical and epistemological problem concerning the relationship between technology and society which involves two approaches. The proactive approach and socio-technological experimentation taken into account in the dilemma are discussed, the former taking health technology assessment (HTA) processes as a reference and the latter the AI studies conducted so far. As a possible prevention of the critical issues raised, the use of the medico-legal method is proposed, which classically lies between the prevention of possible adverse events and the reconstruction of how these occurred.The authors believe that this methodology, adopted as a European guideline in the medico-legal field for the assessment of medical liability, can be adapted to AI applied to the healthcare scenario and used for the assessment of liability issues. The topic deserves further investigation and will certainly be taken into consideration as a possible key to future scenarios.

Reflections on co-creating a model for the value assessment of artificial intelligence technologies.

AIMS: A multidisciplinary group of experts and patients developed the Model for ASsessing the value of Artificial Intelligence (MAS-AI) to ensure an evidence-based and patient-centered approach to introducing artificial intelligence technologies in healthcare. In this article, we share our experiences with meaningfully involving a patient in co-creating a research project concerning complex and technically advanced topics. METHODS: The co-creation was evaluated by means of initial reflections from the research team before the project started, in a continuous logbook, and through semi-structured interviews with patients and two researchers before and after the active co-creation phase of the project. RESULTS: There were initial doubts about the feasibility of including patients in this type of project. Co-creation ensured relevance to patients, a holistic research approach and the debate of ethical considerations. Due to one patient dropping out, it is important to foresee and support the experienced challenges of time and energy spent by the patient in future projects. Having a multidisciplinary team helped the collaboration. A mutual reflective evaluation provided insights into the process which we would otherwise have missed. CONCLUSIONS: We found it possible to create complex and data-intense research projects with patients. Including patients benefitted the project and gave researchers new perspectives on their own research. Mutual reflection throughout the project is key to maximise learning for all parties involved.

Value assessment of artificial intelligence in medical imaging: a scoping review.

BACKGROUND: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS: We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS: Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS: This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.

Model for ASsessing the value of Artificial Intelligence in medical imaging (MAS-AI).

OBJECTIVES: Artificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients developed a Model for ASsessing the value of AI (MAS-AI) in medical imaging. Medical imaging is chosen due to the maturity of AI in this area, ensuring a robust evidence-based model. METHODS: MAS-AI was developed in three phases. First, a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging. Next, we interviewed leading researchers in AI in Denmark. The third phase consisted of two workshops where decision makers, patient organizations, and researchers discussed crucial topics for evaluating AI. The multidisciplinary team revised the model between workshops according to comments. RESULTS: The MAS-AI guideline consists of two steps covering nine domains and five process factors supporting the assessment. Step 1 contains a description of patients, how the AI model was developed, and initial ethical and legal considerations. In step 2, a multidisciplinary assessment of outcomes of the AI application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects, and patient aspects. CONCLUSIONS: We have developed an health technology assessment-based framework to support the introduction of AI technologies into healthcare in medical imaging. It is essential to ensure informed and valid decisions regarding the adoption of AI with a structured process and tool. MAS-AI can help support decision making and provide greater transparency for all parties.

A qualitative exploration of early assessment of innovative medical technologies.

BACKGROUND: Hospitals increasingly make decisions about early development of and investment in innovative medical technologies (IMTs), but at present often without an early assessment of their potential to ensure selection of the most promising candidates for further development. This paper explores how early assessment is carried out in different health organisations and then discusses relevant learning points for hospitals. METHODS: A qualitative study design with a structured interview guide covering four themes was used. Content analyses of interview notes were performed covering four predetermined themes: context, basis for decision-making, process and structure, and perceptions. A fifth theme, handling cognitive bias, was identified during data analysis. RESULTS: A total of 11 organisations participated; eight from the private health industry and three public hospitals. The interviews identified four areas in which early assessment is performed in similar manner across the studied organisations and four areas where differences exist between public hospitals and private organisations. Public hospitals indicate a lower degree of formalised early assessment and less satisfaction with how early assessment is performed, compared to private organisations. Based on the above findings, two learning points may carry promise for hospitals. First, having dedicated prioritising committees for IMTs making stop/go decisions. This committee is separate from the IMT development processes and involved staff. Secondly, the committee should base decisions on a transparent early assessment decision-support tool, which include a broad set of domains, is iterative, describes uncertainty, and minimise cognitive biases. CONCLUSIONS: Similarities and differences in the way early assessment is done in different health organisations were identified. These findings suggest promising learning points for the development of an early assessment model for hospitals.

AI ethics in healthcare.

Artificial Intelligence (AI) holds promise in improving diagnostics and treatment. Likewise, AI is anticipated to mitigate the impacts of staff shortages in the healthcare sector. However, realising the expectations placed on AI requires a substantial effort involving patients and clinical domain experts. Against this setting, this review examines ethical challenges related to the development and implementation of AI in healthcare. Furthermore, we introduce and discuss various approaches, guidelines, and standards that proactively aim to address ethical challenges.

Cognitive behavioral therapy with adaptive virtual reality exposure vs. cognitive behavioral therapy with in vivo exposure in the treatment of social anxiety disorder: A study protocol for a randomized controlled trial.

Background: Social anxiety disorder (SAD) has a high prevalence and an early onset with recovery taking decades to occur. Current evidence supports the efficacy of cognitive behavioral therapy (CBT) with virtual reality (VR) exposure. However, the evidence is based on a sparse number of studies with predominantly small sample sizes. There is a need for more trials investigating the optimal way of applying VR based exposure for SAD. In this trial, we will test the efficacy of CBT with adaptive VR exposure allowing adjustment of the exposure based on real-time monitoring of the participants's anxiety level. Methods: The trial is a randomized controlled, assessor-blinded, parallel-group superiority trail. The study has two arms: (1) CBT including exposure in vivo (CBT-Exp), (2) CBT including exposure therapy using individually tailored VR-content and a system to track anxiety levels (CBT-ExpVR). Treatment is individual, manual-based and consists of 10 weekly sessions with a duration of 60 min. The study includes 90 participants diagnosed with SAD. Assessments are carried out pre-treatment, mid-treatment and at follow-up (6 and 12 months). The primary outcome is the mean score on the Social Interaction Anxiety Scale (SIAS) with the primary endpoint being post-treatment. Discussion: The study adds to the existing knowledge by assessing the efficacy of CBT with adaptive VR exposure. The study has high methodological rigor using a randomized controlled trial with a large sample size that includes follow-up data and validated measures for social anxiety outcomes. Clinical trial registration: ClinicalTrials.gov, identifier: NCT05302518.

Cost-effectiveness of telemonitoring of diabetic foot ulcer patients.

This study compared the cost-effectiveness of telemonitoring with standard monitoring for patients with diabetic foot ulcers. The economic evaluation was nested within a pragmatic randomised controlled trial. A total of 374 patients were randomised to either telemonitoring or standard monitoring. Telemonitoring consisted of two tele-consultations in the patient's own home and one consultation at the outpatient clinic; standard monitoring consisted of three outpatient clinic consultations. Total healthcare costs were estimated over a 6-month period at individual patient level, from a healthcare sector perspective. The bootstrap method was used to calculate the incremental cost-effectiveness ratio, and one-way sensitivity analyses were performed. Telemonitoring costs were found to be €2039 less per patient compared to standard monitoring; however, this difference was not statistically significant. Amputation rate was similar in the two groups. In conclusion, a telemonitoring service in this form had similar costs and effects as standard monitoring.

Review of early assessment models of innovative medical technologies.

INTRODUCTION: Hospitals increasingly make decisions regarding the early development of and investment in technologies, but a formal evaluation model for assisting hospitals early on in assessing the potential of innovative medical technologies is lacking. This article provides an overview of models for early assessment in different health organisations and discusses which models hold most promise for hospital decision makers. METHODS: A scoping review of published studies between 1996 and 2015 was performed using nine databases. The following information was collected: decision context, decision problem, and a description of the early assessment model. RESULTS: 2362 articles were identified and 12 studies fulfilled the inclusion criteria. An additional 12 studies were identified and included in the review by searching reference lists. The majority of the 24 early assessment studies were variants of traditional cost-effectiveness analysis. Around one fourth of the studies presented an evaluation model with a broader focus than cost-effectiveness. Uncertainty was mostly handled by simple sensitivity or scenario analysis. DISCUSSION AND CONCLUSIONS: This review shows that evaluation models using known methods assessing cost-effectiveness are most prevalent in early assessment, but seems ill-suited for early assessment in hospitals. Four models provided some usable elements for the development of a hospital-based model.

[A guide to obtain validity and reproducibility in systematic reviews].

Validity and reproducibility are key concepts in the execution and reporting of the literature searches underlying a systematic review as it enables the reader to assess the quality of the performed searches. However, often the reporting of searches is lacking crucial information. This article provides guidelines for the process from development of a search protocol to quality assessment of the retrieved literature in order to obtain validity and reproducibility. The concepts of recall and precision are introduced to enable quality assessment of the literature searches.