Periodic Manual Algorithm Updates and Generalizability: A Developer's Response. Comment on "Evaluation of Four Artificial Intelligence-Assisted Self-Diagnosis Apps on Three Diagnoses: Two-Year Follow-Up Study"

Needs-Assessment for an Artificial Intelligence-Based Chatbot for Pharmacists in HIV Care: Results from a Knowledge-Attitudes-Practices Survey

BACKGROUND

Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists in considering evidence-based needs.

METHODS

From December 2022 to December 2023, an online needs-assessment survey evaluated Québec pharmacists' knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates.

RESULTS

Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M = 3.74/6). They held largely favorable attitudes towards providing HIV care (M = 4.02/6). They reported a "little" involvement in the delivery of HIV care services (M = 2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M ≥ 4.00/6). On average, MARVIN-Pharma's acceptability and compatibility were in the 'undecided' range (M = 4.34, M = 4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M = 5.6/7).

CONCLUSION

MARVIN-Pharma might help address pharmacists' knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot's development seems vital for its future uptake and usability.

Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques

Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asthma, with or without rhinitis, who received allergen immunotherapy (AIT). Combining these data, this paper proposed a predictive framework for the efficacy of mite subcutaneous immunotherapy in asthma based on machine learning techniques. Introducing the dispersed foraging strategy into the Salp Swarm Algorithm (SSA), a new improved algorithm named DFSSA is proposed. This algorithm effectively alleviates the imbalance between search speed and traversal caused by the fixed partitioning pattern in traditional SSA. Utilizing the fusion of boosting algorithm and kernel extreme learning machine, an AIT performance prediction model was established. To further investigate the effectiveness of the DFSSA-KELM model, this study conducted an auxiliary diagnostic experiment using the immunotherapy predictive medical data collected by the hospital. The findings indicate that selected indicators, such as blood basophil count, sIgE/tIgE (Der p) and sIgE/tIgE (Der f), play a crucial role in predicting treatment outcome. The classification results showed an accuracy of 87.18% and a sensitivity of 93.55%, indicating that the prediction model is an effective and accurate intelligent tool for evaluating the efficacy of AIT.

Algorithm Change Protocols in the Regulation of Adaptive Machine Learning-Based Medical Devices

One of the greatest strengths of artificial intelligence (AI) and machine learning (ML) approaches in health care is that their performance can be continually improved based on updates from automated learning from data. However, health care ML models are currently essentially regulated under provisions that were developed for an earlier age of slowly updated medical devices—requiring major documentation reshape and revalidation with every major update of the model generated by the ML algorithm. This creates minor problems for models that will be retrained and updated only occasionally, but major problems for models that will learn from data in real time or near real time. Regulators have announced action plans for fundamental changes in regulatory approaches. In this Viewpoint, we examine the current regulatory frameworks and developments in this domain. The status quo and recent developments are reviewed, and we argue that these innovative approaches to health care need matching innovative approaches to regulation and that these approaches will bring benefits for patients. International perspectives from the World Health Organization, and the Food and Drug Administration’s proposed approach, based around oversight of tool developers’ quality management systems and defined algorithm change protocols, offer a much-needed paradigm shift, and strive for a balanced approach to enabling rapid improvements in health care through AI innovation while simultaneously ensuring patient safety. The draft European Union (EU) regulatory framework indicates similar approaches, but no detail has yet been provided on how algorithm change protocols will be implemented in the EU. We argue that detail must be provided, and we describe how this could be done in a manner that would allow the full benefits of AI/ML-based innovation for EU patients and health care systems to be realized.