The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis.

Contemporary discourse on Artificial Intelligence (AI) in medicine is oft-sensationalised to the point of bearing no resemblance to its everyday impact and potential - either to proselytise it as a saviour or to condemn its perilous, amoral and sprawling reach.This report aims to unravel the paucity of understanding underpinning this hyperbolic duality, whilst addressing the potential clearly defining its ethical use poses to the semi-public healthcare models in Ireland and Europe. The report contrasts the challenge of regulating the breakneck development of AI, with healthcare's necessity for stringent quality control in ethical technological development to ensure patients' well-being.Physical, practical and philosophical approaches to Artificial Intelligence in medicine are explored through Beauchamp and Childress' principles of delivering care with beneficence, non maleficence, justice and autonomy. AI is scrutinised under Kantian deontological, Benthamite utilitarian and Rawlsian perspectives on health justice. Actor Network theory is used to explain sociotechnical interactions governing human stakeholders developing ethical AI.These analyses operate firstly to define AI concisely, then ground it in its contemporary and future functions in healthcare. They highlight the importance of aligning medical AI with accepted ethical standards as a necessity of its integrated use across healthcare. This report concludes that balanced assessment of AI's role in healthcare requires improvement in three areas: improving clarity in definition of AI and its extant remit in medicine; aligning contemporary discourse on AI use with contemporary objective ethical, legal and system frameworks; and clearly identifying for dismissal a number of logical fallacies deliberately sensationalising AI's potential.

Topical Therapy in Ulcerative Colitis: Always a Bridesmaid but Never a Bride?

A Meta-Analysis of the Placebo Rates of Remission and Response in Clinical Trials of Active Ulcerative Colitis

The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as "artificial intelligence," "deep learning," "machine learning," "forensic medicine," "legal medicine," "forensic pathology" and "medicine" were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

The coming of age of artificial intelligence in medicine.

The role of artificial intelligence in medicine is rapidly expanding. From machine learning diagnostic algorithms to neural networks for electronic health record mining, the diverse applications for artificial intelligence in medicine seem to be endless. The vast amount of physiological and electronic health data generated in the perioperative period has created an opportunity for artificial intelligence to play important roles in anesthesiology, including preoperative risk stratification, intelligent patient monitoring, clinical decision support and personalized perioperative guidelines. In this chapter, we review how data science and artificial intelligence is currently playing a role in personalized anesthesiology and perioperative medicine, as well as lay out a vision for the future directions of this growing field.

Technological developments in medicine have created significant transformations in healthcare services and offered more effective diagnosis and treatment options for patients. Among these advances, artificial intelligence (AI) plays a pivotal role in a variety of medical applications, from disease diagnosis and treatment planning to clinical research and patient care optimization. However, the rapid development of artificial intelligence in medicine also raises ethical challenges and concerns, including patient privacy, data security, inequality and societal impacts. This study examines the potential benefits and risks associated with the global use of artificial intelligence in medicine. The study presents examples and features of global AI-based medical applications, including data-driven diagnosis and treatment, disease prediction and early warning systems, personalized care and treatment planning, drug development and discovery, telemedicine and remote healthcare. Discussions of confidentiality, fairness, integrity, transparency, patient autonomy, responsibility and accountability, change management, social acceptance are emphasized, emphasizing the importance of ethical rules and guidelines in the use of AI in medicine. An analysis of global publication trends in the study of AI and ethics in medicine is also presented, providing insights into the most influential countries and networks of collaboration. As a result, AI has enormous potential in medicine and offers numerous benefits, including better access to healthcare, improved diagnosis and treatment, customized care, resource efficiency, disease prevention and early detection. However, risks related to data security, privacy, inequality and ethical considerations must be addressed. Also, careful management, data security, ethical practices and protection of human factors are vital in leveraging the full potential of AI in medicine.

Baseline Disease Activity and Steroid Therapy Stratify Risk of COVID-19 in Patients With Inflammatory Bowel Disease

Introduction: “Democratizing” artificial intelligence (AI) in medicine and healthcare is a vague term that encompasses various meanings, issues, and visions. This article maps the ways this term is used in discourses on AI in medicine and healthcare and uses this map for a normative reflection on how to direct AI in medicine and healthcare towards desirable futures. Methods: We searched peer-reviewed articles from Scopus, Google Scholar, and PubMed along with grey literature using search terms “democrat*”, “artificial intelligence” and “machine learning”. We approached both as documents and analyzed them qualitatively, asking: What is the object of democratization? What should be democratized, and why? Who is the demos who is said to benefit from democratization? And what kind of theories of democracy are (tacitly) tied to specific uses of the term? Results: We identified four clusters of visions of democratizing AI in healthcare and medicine: 1) democratizing medicine and healthcare through AI, 2) multiplying the producers and users of AI, 3) enabling access to and oversight of data, and 4) making AI an object of democratic governance. Discussion: The envisioned democratization in most visions mainly focuses on patients as consumers and relies on or limits itself to free market-solutions. Democratization in this context requires defining and envisioning a set of social goods, and deliberative processes and modes of participation to ensure that those affected by AI in healthcare have a say on its development and use.

Introduction. Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-inflammatory pathologies of the gastrointestinal tract. This study focuses on the role of artificial intelligence (AI) in the morphological diagnosis of IBD, endoscopic visualization, outcome prediction, and patient monitoring. Aim: To summarize data on the application of AI methods in the diagnosis and treatment of IBD, including digital image analysis, remission prediction, inflammation activity evaluation, and the automation of histological and endoscopic assessment processes. Materials and methods. Modern studies on the use of machine learning (ML) and deep learning (DL) technologies in the diagnosis of IBD were analyzed. Special attention was paid to histological image processing methods, neural network algorithms for inflammation staging, and the use of AI for real-time endoscopic visualization. Results. AI technologies provide more accurate and objective determination of histological inflammation activity using Geboes, Nancy, and Robarts indices. Deep neural networks (CNN) enable automatic classification of inflammation stages and the detection of residual inflammation, which is critical for preventing relapses and reducing colorectal cancer risk. Endocytoscopy and real-time visualization algorithms improve the accuracy of early detection of mucosal dysplasia. Neural networks and other ML algorithms demonstrate high sensitivity and specificity in distinguishing CD from UC and assessing histological remission. Conclusion. AI is becoming an integral part of IBD diagnostics, enhancing the accuracy of morphological studies, optimizing endoscopic methods, and reducing error rates. Integrating AI into clinical practice expands treatment possibilities, including personalized approaches and long-term patient monitoring.

Aim. The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. Materials and methods. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as „artificial intelligence,” „deep learning,” „machine learning,” „forensic medicine,” „legal medicine,” „forensic pathology” and „medicine” were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Conclusions. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

Article1 August 1954ULCERATIVE COLITIS: THERAPEUTIC EFFECTS OF CORTICOTROPIN (ACTH) AND CORTISONE IN 120 PATIENTSJOSEPH B. KIRSNER, M.D., Ph.D., F.A.C.P., WALTER L. PALMER, M.D., Ph.D., F.A.C.P.JOSEPH B. KIRSNER, M.D., Ph.D., F.A.C.P., WALTER L. PALMER, M.D., Ph.D., F.A.C.P.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-41-2-232 SectionsAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail ExcerptINTRODUCTIONThe effects of corticotropin (ACTH) and cortisone in ulcerative colitis have been characterized as beneficial,1-5 indifferent6-8 and unfavorable.9-14 Numerous studies also have emphasized the complications of therapy and the recurrences.15-20 The observation of 40 patients in 19511 indicated that, while ACTH and cortisone do not cure ulcerative colitis, they may be useful adjuncts, initiating rapid and occasionally dramatic clinical improvement. 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KirsnerPalmerMaimonRicketts JBWLSNWE: Clinical course of chronic non-specific ulcerative colitis, J. A. M. A. 137: 922, 1948. CrossrefMedlineGoogle Scholar37. LevineKirsnerKlotz MJBAP: A new concept of the pathogenesis of ulcerative colitis, Science 144: 552, 1951. CrossrefGoogle Scholar38. Machella TE: Problems in ulcerative colitis, Am. J. Med. 13: 760, 1952. CrossrefMedlineGoogle Scholar39. KirsnerPalmer JBWL: Ulcerative colitis: consideration of its etiology and treatment, J. A. M. A. 155: 341, 1954. CrossrefMedlineGoogle Scholar This content is PDF only. To continue reading please click on the PDF icon. Author, Article, and Disclosure InformationAffiliations: Chicago, Illinois*Received for publication February 23, 1954. (Presented at Midwest Regional Meeting, American College of Physicians, Milwaukee, Wisconsin, November 21, 1953.)From the Department of Medicine, University of Chicago.†This study was supported in part by the Wallach Fund for Gastrointestinal Research.Dr. David Klein, of Wilson Laboratories, Chicago, Illinois, very generously supplied much of the corticotropin (ACTH).Merck and Company provided a portion of the cortisone. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byInflammatory bowel disease part II: Clinical and therapeutic aspectsBlood eosinophilia and ulcerative colitis--influence of ethnic origin.Clinical Observations on Inflammatory Bowel DiseaseThe Treabnent of Ulcerative ColitisChronic ulcerative colitis in elderly patientsEosinophilia in Peripheral Blood and Inflammatory Exudate in Non-specific ProctocolitisImmediate and Prolonged Therapeutic Effects of Corticotrophin and Adrenal Steroids in Ulcerative ColitisEOSINOPHIL LEUCOCYTES IN ULCERATIVE COLITISCORTICOTROPIN (ACTH) AND THE ADRENAL STEROIDS IN THE MANAGEMENT OF ULCERATIVE COLITIS: OBSERVATIONS IN 240 PATIENTS*†JOSEPH B. KIRSNER, M.D., F.A.C.P., WALTER L. PALMER, M.D., F.A.C.P., JEAN A. SPENCER, M.D., RICHARD O. BICKS, M.D., CHARLES F. JOHNSON, M.D.Response to Drugs and PsychiatryGeneralized scleroderma associated with chronic ulcerative colitisSteroid hormones in the management of chronic ulcerative colitisThe Use of ACTH and Adrenocorticosteroids in Diseases of the Digestive SystemThe Role of Acth and Adrenal Steroids in Perforation of the Colon in Ulcerative Colitis a Clinical-Pathologic StudyUlcerative Colitis; Observations on Its Etiology, Course and ManagementThe Use of the Adrenocorticosteroids in GastroenterologyThe use of ACTH, cortisone, hydrocortisone and related compounds in the management of ulcerative colitisGastroenterologyToxic Psychosis Under Cortisone and CorticotrophinObservations on the Influence of Corticotropins upon the Course of Chronic Ulcerative ColitisTHE PRESENT STATUS OF ACTH AND ADRENAL STEROID THERAPY IN MEDICINE*J. C. BECK, M.D., F.R.C.P.(C)THE RÔLE OF ACTH, CORTISONE AND HYDROCORTISONE IN SURGERY*†WILLIAM E. ABBOTT, M.D., HARVEY KRIEGER, M.D., STANLEY LEVEY, Ph.D.THE USE OF HYDROCORTISONE IN ULCERATIVE COLITIS: PRELIMINARY OBSERVATIONS 1 August 1954Volume 41, Issue 2Page: 232-250KeywordsAdrenocorticotropic hormoneResearch fundingUlcerative colitis Issue Published: 1 August 1954 PDF downloadLoading ...

This article explores the rapidly evolving field of artificial intelligence (AI) in medicine, focusing on its impact on diagnosis, treatment, and healthcare delivery. Machine learning algorithms are transforming medical practices by analyzing vast datasets, leading to earlier disease detection, personalized treatment plans, and improved resource allocation. However, ethical considerations regarding data privacy, bias, and transparency remain. Understanding AI's potential and limitations is crucial for navigating its ethical implementation and maximizing its benefits for patients and healthcare systems. Keywords: Artificial intelligence, machine learning, healthcare, diagnosis, treatment, precision medicine, ethics, bias, transparency.

Chapter 25 - Outlook of the future landscape of artificial intelligence in medicine and new challenges

The rapid adoption and implementation of artificial intelligence in medicine creates an ontologically distinct situation from prior care models. There are both potential advantages and disadvantages with such technology in advancing the interests of patients, with resultant ontological and epistemic concerns for physicians and patients relating to the instatiation of AI as a dependent, semi- or fully-autonomous agent in the encounter. The concept of libertarian paternalism potentially exercised by AI (and those who control it) has created challenges to conventional assessments of patient and physician autonomy. The unclear legal relationship between AI and its users cannot be settled presently, an progress in AI and its implementation in patient care will necessitate an iterative discourse to preserve humanitarian concerns in future models of care. This paper proposes that physicians should neither uncritically accept nor unreasonably resist developments in AI but must actively engage and contribute to the discourse, since AI will affect their roles and the nature of their work. One’s moral imaginative capacity must be engaged in the questions of beneficence, autonomy, and justice of AI and whether its integration in healthcare has the potential to augment or interfere with the ends of medical practice.