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. METHODS: In a repeated cross sectional analysis, four LLMs (via chatbots/assistant interfaces) were evaluated: OpenAI's GPT-4 (via ChatGPT and Microsoft's Copilot), Google's PaLM 2 and newly released Gemini Pro (via Bard), Anthropic's Claude 2 (via Poe), and Meta's Llama 2 (via HuggingChat). In September 2023, these LLMs were prompted to generate health disinformation on two topics: sunscreen as a cause of skin cancer and the alkaline diet as a cancer cure. Jailbreaking techniques (ie, attempts to bypass safeguards) were evaluated if required. For LLMs with observed safeguarding vulnerabilities, the processes for reporting outputs of concern were audited. 12 weeks after initial investigations, the disinformation generation capabilities of the LLMs were re-evaluated to assess any subsequent improvements in safeguards. MAIN OUTCOME MEASURES: The main outcome measures were whether safeguards prevented the generation of health disinformation, and the transparency of risk mitigation processes against health disinformation. RESULTS: Claude 2 (via Poe) declined 130 prompts submitted across the two study timepoints requesting the generation of content claiming that sunscreen causes skin cancer or that the alkaline diet is a cure for cancer, even with jailbreaking attempts. GPT-4 (via Copilot) initially refused to generate health disinformation, even with jailbreaking attempts-although this was not the case at 12 weeks. In contrast, GPT-4 (via ChatGPT), PaLM 2/Gemini Pro (via Bard), and Llama 2 (via HuggingChat) consistently generated health disinformation blogs. In September 2023 evaluations, these LLMs facilitated the generation of 113 unique cancer disinformation blogs, totalling more than 40 000 words, without requiring jailbreaking attempts. The refusal rate across the evaluation timepoints for these LLMs was only 5% (7 of 150), and as prompted the LLM generated blogs incorporated attention grabbing titles, authentic looking (fake or fictional) references, fabricated testimonials from patients and clinicians, and they targeted diverse demographic groups. Although each LLM evaluated had mechanisms to report observed outputs of concern, the developers did not respond when observations of vulnerabilities were reported. CONCLUSIONS: This study found that although effective safeguards are feasible to prevent LLMs from being misused to generate health disinformation, they were inconsistently implemented. Furthermore, effective processes for reporting safeguard problems were lacking. Enhanced regulation, transparency, and routine auditing are required to help prevent LLMs from contributing to the mass generation of health disinformation.

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. STUDY DESIGN AND SETTING: This cross-sectional study examined the accessibility of CSRs from industry-sponsored clinical trials whose results were reported in the FDA-authorized drug labels of the top 30 highest-revenue medicines of 2021. We determined (1) whether the CSRs were available for download from a public repository, and (2) whether the CSRs were eligible for request by independent researchers based on trial sponsors' data sharing policies. RESULTS: There were 316 industry-sponsored clinical trials with results presented in the FDA-authorized drug labels of the 30 sampled medicines. Of these trials, CSRs were available for public download from 70 (22%), with 37 available at EMA and 40 at Health Canada repositories. While pharmaceutical company platforms offered no direct downloads of CSRs, sponsors confirmed that CSRs from 183 (58%) of the 316 clinical trials were eligible for independent researcher request via the submission of a research proposal. Overall, 218 (69%) of the sampled clinical trials had CSRs available for public download and/or were eligible for request from the trial sponsor. CONCLUSION: CSRs were available from 69% of the clinical trials supporting regulatory approval of the 30 medicines sampled. However, only 22% of the CSRs were directly downloadable from regulatory agencies, the remaining required a formal application process to request access to the CSR from 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. Each post was coercive and targeted at diverse societal groups, including young adults, young parents, older persons, pregnant people, and those with chronic health conditions. The blogs included fake patient and clinician testimonials and obeyed prompting for the inclusion of scientific-looking referencing. Additional generative AI tools created an accompanying 20 realistic images in less than 2 minutes. This process was undertaken by health care professionals and researchers with no specialized knowledge in bypassing AI guardrails, relying solely on publicly available information. Conclusions and Relevance: These observations demonstrate that when the guardrails of AI tools are insufficient, the ability to rapidly generate diverse and large amounts of convincing disinformation is profound. Beyond providing 2 example scenarios, these findings demonstrate an urgent need for robust AI vigilance. The AI tools are rapidly progressing; alongside these advancements, emergent risks are becoming increasingly apparent. Key pillars of pharmacovigilance-including transparency, surveillance, and regulation-may serve as valuable examples for managing these risks and safeguarding public health.

Surgical Antibiotic Prophylaxis in an Era of Antibiotic Resistance: Common Resistant Bacteria and Wider Considerations for Practice.

The increasing incidence of antimicrobial resistance (AMR) presents a global crisis to healthcare, with longstanding antimicrobial agents becoming less effective at treating and preventing infection. In the surgical setting, antibiotic prophylaxis has long been established as routine standard of care to prevent surgical site infection (SSI), which remains one of the most common hospital-acquired infections. The growing incidence of AMR increases the risk of SSI complicated with resistant bacteria, resulting in poorer surgical outcomes (prolonged hospitalisation, extended durations of antibiotic therapy, higher rates of surgical revision and mortality). Despite these increasing challenges, more data are required on approaches at the institutional and patient level to optimise surgical antibiotic prophylaxis in the era of antibiotic resistance (AR). This review provides an overview of the common resistant bacteria encountered in the surgical setting and covers wider considerations for practice to optimise surgical antibiotic prophylaxis in the perioperative setting.

The Role of the Pharmacist in Optimizing Cancer Immunotherapy: A Retrospective Study of Nivolumab Adverse Events.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are an emerging treatment in cancer therapy for prolonging life, minimizing symptoms, and selectively targeting cancer. Program death 1 (PD-1) inhibitors, such as nivolumab, fall within this class, enabling the patient's immune system to detect and destroy cancer. The introduction of ICIs is changing cancer therapy, with new drugs and new toxicities-an evolving area encountered by pharmacists. OBJECTIVE: This study aims to compare the pattern of nivolumab-induced adverse events observed in practice, when compared with clinical trial and literature data. The secondary aim of the study is to identify the presentation and treatment modalities initiated in practice. METHODS: We performed a retrospective case note review across 2 South Australian hospitals to identify the common toxicities and symptomatic treatments experienced by patients receiving nivolumab. Results were compared with clinical trial data from product innovator Bristol-Myer Squib and other published literature. RESULTS: Seventy patients were included in the study; of these, 60 (86%) experienced any grade adverse event(s). A total of 59 (84%) of 70 experienced mild to moderate grade 1 to grade 2 adverse events and 10 (14%) of 70 patients experienced severe grade 3 to grade 4 adverse events, displaying some consistencies with clinical trial and published literature data. Together, the prevalence of adverse events with details on presentation and treatments illustrates possible pharmacy practice strategies and areas for intervention. CONCLUSIONS: The listed prevalence of adverse events and practice strategies identified throughout this study highlights how pharmacists may assist in the identification of predictable ICI toxicities associated with gastrointestinal, endocrine, dermatological toxicities, and fatigue.

Barriers and opportunities for the clinical implementation of therapeutic drug monitoring in oncology.

There are few fields of medicine in which the individualisation of medicines is more important than in the area of oncology. Under-dosing can have significant ramifications due to the potential for therapeutic failure and cancer progression; by contrast, over-dosing may lead to severe treatment-limiting side effects, such as agranulocytosis and neutropenia. Both circumstances lead to poor patient prognosis and contribute to the high mortality rates still seen in oncology. The concept of dose individualisation tailors dosing for each individual patient to ensure optimal drug exposure and best clinical outcomes. While the value of this strategy is well recognised, it has seen little translation to clinical application. However, it is important to recognise that the clinical setting of oncology is unlike that for which therapeutic drug monitoring (TDM) is currently the cornerstone of therapy (e.g. antimicrobials). Whilst there is much to learn from these established TDM settings, the challenges presented in the treatment of cancer must be considered to ensure the implementation of TDM in clinical practice. Recent advancements in a range of scientific disciplines have the capacity to address the current system limitations and significantly enhance the use of anticancer medicines to improve patient health. This review examines opportunities presented by these innovative scientific methodologies, specifically sampling strategies, bioanalytics and dosing decision support, to enable optimal practice and facilitate the clinical implementation of TDM in oncology.

Nuances to precision dosing strategies of targeted cancer medicines.

Selecting the dose of a targeted cancer medicine that is most appropriate for a specific individual is a rational approach to maximize therapeutic outcomes and minimize toxicity. There are many different options for optimizing the dose of targeted cancer medicines and the purpose of this review is to provide a comprehensive comparison of the main options explored in prospective studies. Precision initial dose selection of targeted cancer therapies has been minimally explored to date; however, concentration, toxicity, and therapeutic outcome markers are used to guide on-therapy dose adaption of targeted cancer therapies across several medicines and cancers. While a specific concentration, toxicity, or therapeutic outcome marker commonly dominates an investigated precision on-therapy dose adaption strategy, greater attention to simultaneously account for exposure, toxicity, therapeutic outcomes, disease status, time since treatment initiation and patient preferences are required for optimal patient outcomes. To enable successful implementation of precision dosing strategies for targeted cancer medicines into clinical practice, future prospective studies aiming to develop strategies should consider these elements in their design.