RESUMO
Missed and delayed diagnoses of Hansen's disease (HD) are making the battle against it even more complex, increasing its transmission and significantly impacting those affected and their families. This strains public health systems and raises the risk of lifelong impairments and disabilities. Worryingly, the three countries most affected by HD witnessed a growth in new cases in 2022, jeopardizing the World Health Organization's targets to interrupt transmission. Artificial intelligence (AI) can help address these challenges by offering the potential for rapid case detection, customized treatment, and solutions for accessibility challenges-especially in regions with a shortage of trained healthcare professionals. This perspective article explores how AI can significantly impact the clinical management of HD, focusing on therapeutic strategies. AI can help classify cases, ensure multidrug therapy compliance, monitor geographical treatment coverage, and detect adverse drug reactions and antimicrobial resistance. In addition, AI can assist in the early detection of nerve damage, which aids in disability prevention and planning rehabilitation. Incorporating AI into mental health counseling is also a promising contribution to combating the stigma associated with HD. By revolutionizing therapeutic approaches, AI offers a holistic solution to reduce the burden of HD and improve patient outcomes.
RESUMO
Background: Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. With advances in Artificial Intelligence (AI), tools have emerged that can facilitate diagnosis and classify dermatological images, complementing traditional clinical assessments and being applicable where there is a shortage of specialists. Its adoption requires analysis of efficacy, safety, and ethical considerations, as well as considering the genetic and ethnic diversity of patients. Objective: The systematic review aims to examine research on the detection, classification, and assessment of skin cancer images in clinical settings. Methods: We conducted a systematic literature search on PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis. Results: Through the search, 760 studies were identified in four databases, from which only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation. Conclusion: The application of artificial intelligence in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.
RESUMO
Dental disease is a frequent finding on head and neck images, especially in the context of emergencies, and can be a challenge for radiologists who are inexperienced with findings of dental trauma or disease. Dental abnormalities can be subtle and therefore must be included in the systematic approach to these images. Although dedicated dental images are not acquired in most emergency cases, the teeth are included on many different images of the head and neck, and their initial evaluation seldom requires a specific protocol. The high prevalence of craniofacial trauma, sinus infection, and maxillomandibular procedures, among other conditions, frequently requires interpretation of dental images in daily emergency practice. The imaging findings can be categorized into infection, trauma, and complications of procedures, although sometimes these categories can overlap. Such categories can help the radiologist decide which imaging protocol and dynamic maneuvers should be used and are also useful when reading images and proposing differential diagnoses. Familiarity with the imaging findings of dental emergencies improves the radiologist's diagnostic confidence and role in guiding patient care, avoiding progression to life-threatening conditions, and reducing aesthetic problems, dental loss, and related conditions. Information about the imaging protocols is provided, the relevant anatomy of the teeth and related structures is reviewed, and the key imaging findings of dental emergencies are presented.©RSNA, 2019.
