Physician Perception and Satisfaction with Artificial Intelligence in Cancer Treatment: The Watson for Oncology Experience and Implications for Low-Middle Income Countries (Preprint)

Use of large language models as clinical decision support tools for management pancreatic adenocarcinoma using National Comprehensive Cancer Network guidelines

BACKGROUND

Large language models may form the basis of clinical decision support tools to improve rates of guideline concordant care for pancreatic ductal adenocarcinoma. The objectives of this study were to 1) define the first-pass accuracy of 2 publicly available large language models in responding to prompts on the basis of National Comprehensive Cancer Network guidelines for pancreatic ductal adenocarcinoma, 2) describe consistency of responses within each large language models, and 3) explore differences between the 2 large language models in their accuracy and verbosity.

METHODS

Clinical scenarios were developed on the basis of current National Comprehensive Cancer Network guidelines. Scenario prompts were entered independently by 2 investigators into OpenAI ChatGPT and Microsoft Copilot, yielding 4 responses per scenario. Responses were manually graded on accuracy and verbosity and compared to clinician-derived responses.

RESULTS

From the 104 responses, large language model responses were graded as completely correct in 42% of responses (n = 44). ChatGPT responses were more accurate than Copilot across all prompts (3.33 ± 0.86 vs 3.02 ± 0.87, P = .04). Among 54 generated responses from ChatGPT sessions, 52% (n = 27) were completely correct, 35% (n = 18) contained missing information, and 14% (n = 7) were inaccurate/misleading. Copilot responses were completely correct in 33% (n = 17) of responses, whereas 42% (n = 22) were missing information and 25% (n = 13) contained inaccurate/misleading information. Clinician responses were more concise than all large language model-generated responses (32 ± 13 vs 270 ± 70 words, P < .001).

CONCLUSION

Large language model-powered responses to clinical questions regarding pancreatic ductal adenocarcinoma are often inaccurate and verbose. These publicly available large language models require significant optimization before implementation within health care as clinical decision support tools.

Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis

Neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), present significant challenges owing to their complex pathologies and a lack of curative treatments. Early detection and reliable biomarkers are critical but remain elusive. Artificial intelligence (AI) has emerged as a transformative tool, enabling advancements in biomarker discovery, diagnostic accuracy, and therapeutic development. From optimizing clinical-trial designs to leveraging omics and neuroimaging data, AI facilitates understanding of disease and treatment innovation. Notably, technologies such as AlphaFold and deep learning models have revolutionized proteomics and neuroimaging, offering unprecedented insights into ALS pathophysiology. This review highlights the intersection of AI and ALS, exploring the current state of progress and future therapeutic prospects.

Transforming Healthcare in Low-Resource Settings With Artificial Intelligence: Recent Developments and Outcomes

BACKGROUND

Artificial intelligence now encompasses technologies like machine learning, natural language processing, and robotics, allowing machines to undertake complex tasks traditionally done by humans. AI's application in healthcare has led to advancements in diagnostic tools, predictive analytics, and surgical precision.

AIM

This comprehensive review aims to explore the transformative impact of AI across diverse healthcare domains, highlighting its applications, advancements, challenges, and contributions to enhancing patient care.

METHODOLOGY

A comprehensive literature search was conducted across multiple databases, covering publications from 2014 to 2024. Keywords related to AI applications in healthcare were used to gather data, focusing on studies exploring AI's role in medical specialties.

RESULTS

AI has demonstrated substantial benefits across various fields of medicine. In cardiology, it aids in automated image interpretation, risk prediction, and the management of cardiovascular diseases. In oncology, AI enhances cancer detection, treatment planning, and personalized drug selection. Radiology benefits from improved image analysis and diagnostic accuracy, while critical care sees advancements in patient triage and resource optimization. AI's integration into pediatrics, surgery, public health, neurology, pathology, and mental health has similarly shown significant improvements in diagnostic precision, personalized treatment, and overall patient care. The implementation of AI in low-resource settings has been particularly impactful, enhancing access to advanced diagnostic tools and treatments.

CONCLUSION

AI is rapidly changing the healthcare industry by greatly increasing the accuracy of diagnoses, streamlining treatment plans, and improving patient outcomes across a variety of medical specializations. This review underscores AI's transformative potential, from early disease detection to personalized treatment plans, and its ability to augment healthcare delivery, particularly in resource-limited settings.

Sister partnership to overcome the global burden of cancer

Emerging countries are currently facing an increasing burden of cancer while they do not have adequate prevention, monitoring, and research capabilities to tackle the disease. Cancer outcomes are influenced by several factors, including different cancer patterns, national cancer screening guidelines, current stage of disease, and access to quality care and treatments. Discrepancies in cancer care between emerging and developed countries require actions to achieve global health equity. The process of pioneering a sister relationship in the oncology field can thwart the global burden of cancer. The objective of such cooperation programs should include research and training programs, evidence-based oncology practice, and quality cancer. Building global connections will therefore be the novel approach to addressing the global burden of cancer.

Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study

BACKGROUND

Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians.

OBJECTIVE

Using new software called Therapha, this study aimed to assess the accuracy level of artificial intelligence as a Clinical Decision Support System (CDSS) compared to MRI in predicting lumbar disc herniated patients.

METHODS

One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in three stages. Firstly, a case series was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Finally, to determine the accuracy of the newly developed software, a cross-sectional study was undertaken involving 100 patients.

RESULTS

The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%.

CONCLUSIONS

The study's findings revealed that CDSS using Therapha has a reasonable level of efficacy, and this can be utilized clinically to acquire a faster and more accurate screening of patients with lumbar disc herniation.

Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations

BACKGROUND

Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery.

AIM

This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them.

METHODOLOGY

Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties.

RESULTS

The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI's full use in healthcare.

CONCLUSION

Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.

Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore

OBJECTIVES

Artificial intelligence (AI)-driven clinical decision support systems (CDSSs) can augment antibiotic decision-making capabilities, but physicians' hesitancy in adopting them may undermine their utility. We conducted a cross-country comparison of physician perceptions on the barriers and facilitators in accepting an AI-enabled CDSS for antibiotic prescribing.

METHODS

We conducted in-depth interviews with physicians from the National Centre for Infectious Diseases (NCID), Singapore, and Christian Medical College Vellore (CMCV), India, between April and December 2022. Our semi-structured in-depth interview guides were anchored on Venkatesh's UTAUT model. We used clinical vignettes to illustrate the application of AI in clinical decision support for antibiotic prescribing and explore medico-legal concerns.

RESULTS

Most NCID physicians felt that an AI-enabled CDSS could facilitate antibiotic prescribing, while most CMCV physicians were sceptical about the tool's utility. The hesitancy in adopting an AI-enabled CDSS stems from concerns about the lack of validated and successful examples, fear of losing autonomy and clinical skills, difficulty of use, and impediment in work efficiency. Physicians from both sites felt that a user-friendly interface, integration with workflow, transparency of output, a guiding medico-legal framework, and training and technical support would improve the uptake of an AI-enabled CDSS.

CONCLUSION

In conclusion, the acceptance of AI-enabled CDSSs depends on the physician's confidence with the tool's recommendations, perceived ease of use, familiarity with AI, the organisation's digital culture and support, and the presence of medico-legal governance of AI. Progressive implementation and continuous feedback are essential to allay scepticism around the utility of AI-enabled CDSSs.