Assessing the System-Instruction Vulnerabilities of Large Language Models to Malicious Conversion Into Health Disinformation Chatbots.

Large language models (LLMs) offer substantial promise for improving health care; however, some risks warrant evaluation and discussion. This study assessed the effectiveness of safeguards in foundational LLMs against malicious instruction into health disinformation chatbots. Five foundational LLMs-OpenAI's GPT-4o, Google's Gemini 1.

Patient-reported outcome thresholds and their associations with survival, adverse events, and quality of life in a pooled analysis of breast cancer trials.

Researchers at the EORTC recently recommended clinical thresholds for the QLQ-C30 to facilitate actionable insights in clinical practice. We evaluate the distribution of these thresholds and associations with outcomes in breast cancer. Data were pooled from two early-stage and six advanced-stage breast cancer trials.

Generative AI's healthcare professional role creep: a cross-sectional evaluation of publicly accessible, customised health-related GPTs.

Introduction: Generative artificial intelligence (AI) is advancing rapidly; an important consideration is the public's increasing ability to customise foundational AI models to create publicly accessible applications tailored for specific tasks. This study aims to evaluate the accessibility and functionality descriptions of customised GPTs on the OpenAI GPT store that provide health-related information or assistance to patients and healthcare professionals.

Methods: We conducted a cross-sectional observational study of the OpenAI GPT store from September 2 to 6, 2024, to identify publicly accessible customised GPTs with health-related functions.

Generative AI chatbots for reliable cancer information: Evaluating web-search, multilingual, and reference capabilities of emerging large language models.

Recent advancements in large language models (LLMs) enable real-time web search, improved referencing, and multilingual support, yet ensuring they provide safe health information remains crucial. This perspective evaluates seven publicly accessible LLMs-ChatGPT, Co-Pilot, Gemini, MetaAI, Claude, Grok, Perplexity-on three simple cancer-related queries across eight languages (336 responses: English, French, Chinese, Thai, Hindi, Nepali, Vietnamese, and Arabic). None of the 42 English responses contained clinically meaningful hallucinations, whereas 7 of 294 non-English responses did.

Antithrombotic utilization, adverse events, and associations with treatment outcomes in multiple myeloma: pooled analysis of three clinical trials.

Background: Patients with multiple myeloma (MM) are at risk of venous thromboembolism (VTE), worsened by immunomodulatory drugs. Although antithrombotics are recommended for prophylaxis, existing guidelines are suboptimal and treatment outcomes remain unclear.

Objectives: This study aimed to investigate adverse events, antithrombotic utilization, and their associations with survival outcomes in patients with MM initiating multi-drug immunomodulatory combinations.

Clinical Study Report and Individual Participant Data Transparency for US Food and Drug Administration-Approved Anticancer Drugs: A Call for Systematic Data Availability.

Unlocking the full potential of clinical trials through comprehensive CSR and IPD sharing can revolutionize cancer care, enhance safety evaluations, and reduce bias in systematic reviews. It is time for all stakeholders to embrace transparency and advance patient-centered outcomes.

Current safeguards, risk mitigation, and transparency measures of large language models against the generation of health disinformation: repeated cross sectional analysis.

Objectives: To evaluate the effectiveness of safeguards to prevent large language models (LLMs) from being misused to generate health disinformation, and to evaluate the transparency of artificial intelligence (AI) developers regarding their risk mitigation processes against observed vulnerabilities.

Design: Repeated cross sectional analysis.

Setting: Publicly accessible LLMs.

Accessibility of clinical study reports supporting medicine approvals: a cross-sectional evaluation.

Objectives: Clinical study reports (CSRs) are highly detailed documents that play a pivotal role in medicine approval processes. Though not historically publicly available, in recent years, major entities including the European Medicines Agency (EMA), Health Canada, and the US Food and Drug Administration (FDA) have highlighted the importance of CSR accessibility. The primary objective herein was to determine the proportion of CSRs that support medicine approvals available for public download as well as the proportion eligible for independent researcher request via the study sponsor.

Health Disinformation Use Case Highlighting the Urgent Need for Artificial Intelligence Vigilance: Weapons of Mass Disinformation.

Importance: Although artificial intelligence (AI) offers many promises across modern medicine, it may carry a significant risk for the mass generation of targeted health disinformation. This poses an urgent threat toward public health initiatives and calls for rapid attention by health care professionals, AI developers, and regulators to ensure public safety.

Observations: As an example, using a single publicly available large-language model, within 65 minutes, 102 distinct blog articles were generated that contained more than 17 000 words of disinformation related to vaccines and vaping.

A 10-year update to the principles for clinical trial data sharing by pharmaceutical companies: perspectives based on a decade of literature and policies.

Data sharing is essential for promoting scientific discoveries and informed decision-making in clinical practice. In 2013, PhRMA/EFPIA recognised the importance of data sharing and supported initiatives to enhance clinical trial data transparency and promote scientific advancements. However, despite these commitments, recent investigations indicate significant scope for improvements in data sharing by the pharmaceutical industry.

Heterogeneity and Utility of Pharmaceutical Company Sharing of Individual-Participant Data Packages.

Importance: The pharmaceutical industry has made substantial investments in developing processes for sharing individual-participant data (IPD) from clinical trials. However, the utility and completeness of shared IPD and supporting documents must be evaluated to ensure the potential for scientific advancements from the data sharing ecosystem can be realized.

Objective: To assess the utility and completeness of IPD and supporting documents provided from industry-sponsored clinical trials.

Audit of Data Sharing by Pharmaceutical Companies for Anticancer Medicines Approved by the US Food and Drug Administration.

Importance: Emerging policies drafted by the pharmaceutical industry indicate that they will transparently share clinical trial data. These data offer an unparalleled opportunity to advance evidence-based medicine and support decision-making.

Objective: To evaluate the eligibility of independent, qualified researchers to access individual participant data (IPD) from oncology trials that supported US Food and Drug Administration (FDA) approval of new anticancer medicines within the past 10 years.

Association between Patient-Reported Outcomes and Survival in Patients with Advanced Urothelial Carcinoma Treated with Atezolizumab.

Background: Atezolizumab is an immune checkpoint inhibitor (ICI) and a frontline treatment of patients with cisplatin-ineligible advanced urothelial carcinoma (UC). There is limited evidence on the prognostic value of patient reported outcomes (PROs) in advanced UC treatment, particularly in the context of ICI therapy.

Objective: To investigate the prognostic association of PROs with survival in patients with advanced UC treated with atezolizumab.

Prediction of severe neutropenia and diarrhoea in breast cancer patients treated with abemaciclib.

Introduction: Neutropenia and diarrhoea are common and potentially serious adverse events associated with abemaciclib in advanced breast cancer (ABC), and the risk factors have been minimally explored. The study aimed to develop clinical prediction tools that allow personalized predictions of neutropenia and diarrhoea following abemaciclib initiation.

Materials And Methods: Data was pooled from MONARCH 1, 2 and 3 trials investigating abemaciclib.

The obesity paradox in early and advanced HER2 positive breast cancer: pooled analysis of clinical trial data.

While many studies have evaluated the relationship between BMI and breast cancer outcomes, it is unclear whether this relationship is consistent between early breast cancer (BC) and advanced BC. The study included 5099 patients with HER2 positive early BC (EBC) and 3496 with HER2 positive advanced BC (ABC). In the EBC cohort, higher BMI was associated with worse overall survival (OS) (HR [95% CI]: overweight = 1.

The Influence of Pre-Existing Beta-Blockers Use on Survival Outcomes in HER2 Positive Advanced Breast Cancer: Pooled Analysis of Clinical Trial Data.

Beta-blockers (BB) are commonly used to manage cardiovascular disease and may have benefits in controlling complications of anti-HER2 therapies. This study aimed to evaluate the association of pre-existing BB use with survival outcomes in patients initiating anti-HER2 therapy for advanced breast cancer (ABC). Data from clinical trials EMILIA, TH3RESA, MARIANNE, and CLEOPATRA was pooled.

Early Adverse Events predict Survival Outcomes in HER2-positive Advanced Breast Cancer Patients treated with Lapatinib plus Capecitabine.

: This study aimed to investigate the impact of early adverse events (AE) following the initiation of lapatinib plus capecitabine on the progression-free survival (PFS) and overall survival (OS) outcomes of human epidermal growth factor receptor 2 (HER2) positive advanced breast cancer (ABC) patients. : A secondary analysis of participants treated with lapatinib plus capecitabine, or ado-trastuzumab emtansine in the clinical trial EMILIA was conducted. Cox proportional hazard analysis was used to assess the impact of AE occurring within the first 42 days of lapatinib plus capecitabine therapy on the PFS and OS outcomes of ABC patients.

A literature review of treatment-specific clinical prediction models in patients with breast cancer.

Despite advances in the breast cancer treatment, significant variability in patient outcomes remain. This results in significant stress to patients and clinicians. Treatment-specific clinical prediction models allow patients to be matched against historical outcomes of patients with similar characteristics; thereby reducing uncertainty by providing personalised estimates of benefits, harms, and prognosis.

Predicting Thrombocytopenia in Patients With Breast Cancer Treated With Ado-trastuzumab Emtansine.

Introduction: Thrombocytopenia is a common and potentially serious adverse event of ado-trastuzumab emtansine (T-DM1) use in patients with advanced breast cancer. However, the risk factors have been minimally explored. Our aim was to develop a clinical prediction model from the clinicopathologic data that would allow for quantification of the personalized risks of thrombocytopenia from T-DM1 usage.

Design, synthesis and evaluation of a series of 5-methoxy-2,3-naphthalimide derivatives as AcrB inhibitors for the reversal of bacterial resistance.

A series of novel 5-methoxy-2,3-naphthalimide derivatives were designed, synthesized and evaluated for their biological activities. In particular, the ability of the compounds to synergize with antimicrobials, to inhibit Nile Red efflux, and to target AcrB was assayed. The results showed that the most of the tested compounds more sensitized the Escherichia coli BW25113 to the antibiotics than the parent compounds 7c and 15, which were able to inhibit Nile Red efflux.