Advancing Research and Treatment: An Overview of Clinical Trials in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Future Perspectives

[Interdisciplinary, collaborative D-A-CH (Germany, Austria and Switzerland) consensus statement concerning the diagnostic and treatment of myalgic encephalomyelitis/chronic fatigue syndrome]

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe, chronic multisystemic disease which, depending on its severity, can lead to considerable physical and cognitive impairment, loss of ability to work and the need for nursing care including artificial nutrition and, in very severe cases, even death.The aim of this D-A-CH (Germany, Austria, Switzerland) consensus statement is 1) to summarize the current state of knowledge on ME/CFS, 2) to highlight the Canadian Consensus Criteria (CCC) as clinical criteria for diagnostics with a focus on the leading symptom post-exertional malaise (PEM) and 3) to provide an overview of current options and possible future developments, particularly with regard to diagnostics and therapy. The D-A-CH consensus statement is intended to support physicians, therapists and valuer in diagnosing patients with suspected ME/CFS by means of adequate anamnesis and clinical-physical examinations as well as the recommended clinical CCC, using the questionnaires and other examination methods presented. The overview of the two pillars of therapy for ME/CFS, pacing and symptom-relieving therapy options, is intended not only to provide orientation for physicians and therapists, but also to support decision-makers from healthcare policy and insurance companies in determining which therapy options should already be reimbursable by them at this point in time for the indication ME/CFS.

Revealing the Complexity of Fatigue: A Review of the Persistent Challenges and Promises of Artificial Intelligence

Part I reviews persistent challenges obstructing progress in understanding complex fatigue's biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.