Influences on User Trust in Healthcare Artificial Intelligence: A Systematic Review

BACKGROUND: Artificial Intelligence (AI) is becoming increasingly prominent in domains such as healthcare. It is argued to be transformative through altering the way in which healthcare data is used. The realisation and success of AI depend heavily on people’s trust in its applications. Yet, influences on trust in healthcare AI (HAI) applications so far have been underexplored. The objective of this study was to identify aspects related to users, AI applications and the wider context influencing trust in HAI. METHODS: We performed a systematic review to map out influences on user trust in HAI. To identify relevant studies, we searched seven electronic databases in November 2019 (ACM digital library, IEEE Explore, NHS Evidence, ProQuest Dissertations & Thesis Global, PsycINFO, PubMed, Web of Science Core Collection). Searches were restricted to publications available in English and German. To be included studies had to be empirical; focus on an AI application (excluding robotics) in a health-related setting; and evaluate applications with regards to users. RESULTS: Three studies, one mixed-method and two qualitative studies in English were included. Influences on trust fell into three broad categories: human-related (knowledge, expectation, mental model, self-efficacy, type of user, age, gender), AI-related (data privacy and safety, operational safety, transparency, design, customizability, trialability, explainability, understandability, power-control-balance, benevolence) and context-related (AI company, media, users’ social network). The factors resulted in an updated logic model illustrating the relationship between these aspects. CONCLUSIONS: Trust in HAI depends on a variety of factors, both external and internal to AI applications. This study contributes to our understanding of what influences trust in HAI by highlighting key influences, as well as pointing to gaps and issues in existing research on trust and AI. In so doing, it offers a starting point for further investigation of trust environments as well as trustworthy AI applications

The Artificial Intelligence in Public Health Toolkit: A novel resource for stakeholder engagement

Background: Artificial intelligence (AI) has considerable potential to enhance public health. People using AI systems for public health decisions, or who are affected by such decisions, may need to understand how these systems work, or articulate how much they want decision-makers to trust the system. This public engagement project, part of the Human Behaviour-Change Project, aimed to a) explore people’s views regarding trust in, and use of, AI for public health decisions and, based on that, b) create a toolkit of resources to facilitate people critically questioning the use of an AI system. Methods: Six online, public engagement workshops were conducted in England in 2021 to inform the content and design of the toolkit. Twenty-four people including members of the public, public health professionals, and researchers worked with a graphic designer to create the toolkit. Results: The resulting ‘AI in Public Health Toolkit’ contains resources to enable people to evaluate AI systems and provides a roadmap for the decision process, a set of suggested questions to ask about an AI system, a guide to features of good answers and a ‘personal views tool’ prompting reflection on the answers received. Participants suggested that public health decision-makers should use the Toolkit to consult people representative of those affected by the decision to recommend whether an AI system should be used in that instance. Conclusions: The ‘AI in Public Health Toolkit’ has the potential to facilitate public engagement in the use of AI in public health. The Toolkit gives those developing AI-driven systems a sense of the public’s queries regarding such systems. The resources in the Toolkit can also facilitate conversations about broader AI applications to healthcare and public services

Design and rationale for a global novel non-invasive screening observational study using genetics and non-invasive methodologies to identify at-risk MASLD participants: The ALIGN study.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common chronic liver disease that is heterogenous in nature with various drivers and modifiers such as metabolic dysfunction and genetic factors. MASLD and the progressive subtype, metabolic dysfunction-associated steatohepatitis (MASH) represent the most rapidly increasing cause of liver-related mortality. There are limited treatment options for patients living with MASLD and MASH, various treatments with an array of different targets are under investigation and one therapeutic has been approved since the initiation of this study. Clinical trials investigating treatments for MASLD and MASH are associated with a high screen failure rate, driven largely by the regulatory required histological inclusion criteria for clinical trial eligibility. Other available clinically utilized biomarkers, typically referred to as non-invasive tests (NITs), can assess both the presence of steatosis and the severity of liver fibrosis in patients with MASLD and MASH in the clinic but are not yet approved over histological changes as endpoints for pivotal trials. However, the use of NITs have been demonstrated to increase the likelihood of meeting clinical trial entry criteria. All-Liver Interventional Global Network (ALIGN) is the first described multi-centre global observational screening study aimed at identifying individuals with a high likelihood of MASLD/MASH interested in participating in therapeutic clinical trials using non-invasive methodologies and genetic testing. This study represents a valuable prototype for industry and academic groups looking to evaluate large populations for MASH eligibility and interest in clinical trial participation

The Human and Mouse Islet Peptidome: Effects of Obesity and Type 2 Diabetes, and Assessment of Intraislet Production of Glucagon-like Peptide-1.

To characterize the impact of metabolic disease on the peptidome of human and mouse pancreatic islets, LC-MS was used to analyze extracts of human and mouse islets, purified mouse alpha, beta, and delta cells, supernatants from mouse islet incubations, and plasma from patients with type 2 diabetes. Islets were obtained from healthy and type 2 diabetic human donors, and mice on chow or high fat diet. All major islet hormones were detected in lysed islets as well as numerous peptides from vesicular proteins including granins and processing enzymes. Glucose-dependent insulinotropic peptide (GIP) was not detectable. High fat diet modestly increased islet content of proinsulin-derived peptides in mice. Human diabetic islets contained increased content of proglucagon-derived peptides at the expense of insulin, but no evident prohormone processing defects. Diabetic plasma, however, contained increased ratios of proinsulin and des-31,32-proinsulin to insulin. Active GLP-1 was detectable in human and mouse islets but 100-1000-fold less abundant than glucagon. LC-MS offers advantages over antibody-based approaches for identifying exact peptide sequences, and revealed a shift toward islet insulin production in high fat fed mice, and toward proglucagon production in type 2 diabetes, with no evidence of systematic defective prohormone processing

Efficacy and safety of cotadutide, a dual glucagon-like peptide-1 and glucagon receptor agonist, in a randomized phase 2a study of patients with type 2 diabetes and chronic kidney disease

AIM: To assess the efficacy, safety and tolerability of cotadutide in patients with type 2 diabetes mellitus and chronic kidney disease. MATERIALS AND METHODS: In this phase 2a study (NCT03550378), patients with body mass index 25‐45 kg/m(2), estimated glomerular filtration rate 30‐59 ml/min/1.73 m(2) and type 2 diabetes [glycated haemoglobin 6.5‐10.5% (48‐91 mmol/mol)] controlled with insulin and/or oral therapy combination, were randomized 1:1 to once‐daily subcutaneous cotadutide (50‐300 μg) or placebo for 32 days. The primary endpoint was plasma glucose concentration assessed using a mixed‐meal tolerance test. RESULTS: Participants receiving cotadutide (n = 21) had significant reductions in the mixed‐meal tolerance test area under the glucose concentration‐time curve (–26.71% vs. +3.68%, p < .001), more time in target glucose range on continuous glucose monitoring (+14.79% vs. –21.23%, p = .001) and significant reductions in absolute bodyweight (–3.41 kg vs. –0.13 kg, p < .001) versus placebo (n = 20). In patients with baseline micro‐ or macroalbuminuria (n = 18), urinary albumin‐to‐creatinine ratios decreased by 51% at day 32 with cotadutide versus placebo (p = .0504). No statistically significant difference was observed in mean change in estimated glomerular filtration rate between treatments. Mild/moderate adverse events occurred in 71.4% of participants receiving cotadutide and 35.0% receiving placebo. CONCLUSIONS: We established the efficacy of cotadutide in this patient population, with significantly improved postprandial glucose control and reduced bodyweight versus placebo. Reductions in urinary albumin‐to‐creatinine ratios suggest potential benefits of cotadutide on kidney function, supporting further evaluation in larger, longer‐term clinical trials

Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes

Follicular helper T (TFH) cells are implicated in type 1 diabetes (T1D), and their development has been linked to CD28 costimulation. We tested whether TFH cells were decreased by costimulation blockade using the CTLA-4–immunoglobulin (Ig) fusion protein (abatacept) in a mouse model of diabetes and in individuals with new-onset T1D. Unbiased bioinformatics analysis identified that inducible costimulatory molecule (ICOS)+ TFH cells and other ICOS+ populations, including peripheral helper T cells, were highly sensitive to costimulation blockade. We used pretreatment TFH profiles to derive a model that could predict clinical response to abatacept in individuals with T1D. Using two independent approaches, we demonstrated that higher frequencies of ICOS+ TFH cells at baseline were associated with a poor clinical response following abatacept administration. Therefore, TFH analysis may represent a new stratification tool, permitting the identification of individuals most likely to benefit from costimulation blockade

Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes

Follicular helper T (TFH) cells are implicated in type 1 diabetes (T1D), and their development has been linked to CD28 costimulation. We tested whether TFH cells were decreased by costimulation blockade using the CTLA-4–immunoglobulin (Ig) fusion protein (abatacept) in a mouse model of diabetes and in individuals with new-onset T1D. Unbiased bioinformatics analysis identified that inducible costimulatory molecule (ICOS)+ TFH cells and other ICOS+ populations, including peripheral helper T cells, were highly sensitive to costimulation blockade. We used pretreatment TFH profiles to derive a model that could predict clinical response to abatacept in individuals with T1D. Using two independent approaches, we demonstrated that higher frequencies of ICOS+ TFH cells at baseline were associated with a poor clinical response following abatacept administration. Therefore, TFH analysis may represent a new stratification tool, permitting the identification of individuals most likely to benefit from costimulation blockade

Hospital admissions for severe infections in people with chronic kidney disease in relation to renal disease severity and diabetes status

Background: Immunosuppressive agents are being investigated for the treatment of chronic kidney disease (CKD) but may increase risk of infection. This was a retrospective observational study intended to evaluate the risk of hospitalized infection in patients with CKD, by estimated glomerular filtration rate (eGFR) and proteinuria status, aiming to identify the most appropriate disease stage for immunosuppressive intervention. Methods: Routine UK primary-care and linked secondary-care data were extracted from the Clinical Practice Research Datalink. Patients with a record of CKD were identified and grouped into type 2, type 1 and nondiabetes cohorts. Time-dependent, Cox proportional hazard models were used to determine the likelihood of hospitalized infection. Results: We identified 97 839 patients with a record of CKD, of these 11 719 (12%) had type 2 diabetes. In these latter patients, the adjusted hazard ratios (aHR) were 1.00 (95% CI: 0.80-1.25), 1.00, 1.03 (95% CI: 0.92-1.15), 1.36 (95% CI: 0.20-1.54), 1.82 (95% CI: 1.54-2.15) and 2.41 (95% CI: 1.60-3.63) at eGFR stages G1, G2 (reference), G3a, G3b, G4 and G5, respectively; and 1.00, 1.45 (95% CI: 1.29-1.63) and 1.91 (95% CI: 1.67-2.20) at proteinuria stages A1 (reference), A2 and A3, respectively. All aHRs (except G1 and G3a) were significant, with similar patterns observed within the non-DM and overall cohorts. Conclusions: eGFR and degree of albuminuria were independent markers of hospitalized infection in both patients with and without diabetes. The same patterns of hazard ratios of eGFR and proteinuria were seen in CKD patients regardless of diabetes status, with the risk of each outcome increasing with a decreasing eGFR and increasing proteinuria. Infection risk increased significantly from eGFR stage G3b and proteinuria stage A2 in type 2 diabetes. Treating type 2 DM patients with CKD at eGFR stages G1-G3a with immunosuppressive therapy may therefore provide a favourable risk-benefit ratio (G1-G3a in type 2 diabetes; G1-G2 in nondiabetes and overall cohorts) although the degree of proteinuria needs to be considered

Hospital admissions for severe infections in people with chronic kidney disease in relation to renal disease severity and diabetes status

Background: Immunosuppressive agents are being investigated for the treatment of chronic kidney disease (CKD) but may increase risk of infection. This was a retrospective observational study intended to evaluate the risk of hospitalized infection in patients with CKD, by estimated glomerular filtration rate (eGFR) and proteinuria status, aiming to identify the most appropriate disease stage for immunosuppressive intervention. Methods: Routine UK primary-care and linked secondary-care data were extracted from the Clinical Practice Research Datalink. Patients with a record of CKD were identified and grouped into type 2, type 1 and nondiabetes cohorts. Time-dependent, Cox proportional hazard models were used to determine the likelihood of hospitalized infection. Results: We identified 97 839 patients with a record of CKD, of these 11 719 (12%) had type 2 diabetes. In these latter patients, the adjusted hazard ratios (aHR) were 1.00 (95% CI: 0.80-1.25), 1.00, 1.03 (95% CI: 0.92-1.15), 1.36 (95% CI: 0.20-1.54), 1.82 (95% CI: 1.54-2.15) and 2.41 (95% CI: 1.60-3.63) at eGFR stages G1, G2 (reference), G3a, G3b, G4 and G5, respectively; and 1.00, 1.45 (95% CI: 1.29-1.63) and 1.91 (95% CI: 1.67-2.20) at proteinuria stages A1 (reference), A2 and A3, respectively. All aHRs (except G1 and G3a) were significant, with similar patterns observed within the non-DM and overall cohorts. Conclusions: eGFR and degree of albuminuria were independent markers of hospitalized infection in both patients with and without diabetes. The same patterns of hazard ratios of eGFR and proteinuria were seen in CKD patients regardless of diabetes status, with the risk of each outcome increasing with a decreasing eGFR and increasing proteinuria. Infection risk increased significantly from eGFR stage G3b and proteinuria stage A2 in type 2 diabetes. Treating type 2 DM patients with CKD at eGFR stages G1-G3a with immunosuppressive therapy may therefore provide a favourable risk-benefit ratio (G1-G3a in type 2 diabetes; G1-G2 in nondiabetes and overall cohorts) although the degree of proteinuria needs to be considered

Phenotypic screening reveals TNFR2 as a promising target for cancer immunotherapy.

Antibodies that target cell-surface molecules on T cells can enhance anti-tumor immune responses, resulting in sustained immune-mediated control of cancer. We set out to find new cancer immunotherapy targets by phenotypic screening on human regulatory T (Treg) cells and report the discovery of novel activators of tumor necrosis factor receptor 2 (TNFR2) and a potential role for this target in immunotherapy. A diverse phage display library was screened to find antibody mimetics with preferential binding to Treg cells, the most Treg-selective of which were all, without exception, found to bind specifically to TNFR2. A subset of these TNFR2 binders were found to agonise the receptor, inducing iκ-B degradation and NF-κB pathway signalling in vitro. TNFR2 was found to be expressed by tumor-infiltrating Treg cells, and to a lesser extent Teff cells, from three lung cancer patients, and a similar pattern was also observed in mice implanted with CT26 syngeneic tumors. In such animals, TNFR2-specific agonists inhibited tumor growth, enhanced tumor infiltration by CD8+ T cells and increased CD8+ T cell IFN-γ synthesis. Together, these data indicate a novel mechanism for TNF-α-independent TNFR2 agonism in cancer immunotherapy, and demonstrate the utility of target-agnostic screening in highlighting important targets during drug discovery.GW, BM, SG, JC-U, AS, AG-M, CB, JJ, RL, AJL, SR, RS, LJ, VV-A, RM and RWW were funded by MedImmune; JP and VB were funded by AstraZeneca PLC; JW, RSA-L and JB were funded by NIHR Cambridge Biomedical Research Centre and Kidney Research UK; JS and JF were funded by Retrogenix Ltd