Standardised clinical assessments and advanced AI-driven instruments used to evaluate neurofunctional deficits, including within biomarker based framework, in Parkinson's Disease -Human intelligence made vs. AI models -systematic review

Aurelian Anghelescu^1,2Constantin Munteanu^2,3Aura Spinu^1,2*Vlad Ciobanu⁴Cristina Popescu^1,2Ioana Elena Cioca¹Ioana Andone^1,2Simona-Isabelle Stoica^1,2Mihaela Mandu^1,2Ana Rebedea²Sebastian Giuvara²Alin-Daniel Malaelea²Andreea-Iulia Vladulescu-Trandafir^1,2Maria Veronica Morcov^1,5*Gelu Onose^1,2

• ¹Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
• ²Emergency Clinical Hospital Bagdasar Arseni, Bucharest, Romania
• ³Grigore T. Popa University of Medicine and Pharmacy, Iași, Iasi, Romania
• ⁴Polytechnic University of Bucharest, Bucharest, Romania
• ⁵National Teaching Centre for Neuro-psycho-motor Rehabilitation in Children “Dr. N. Robanescu”, Bucharest, Romania

Generative Pre-Trained Transformer (ChatGPT) 4.o and ChatGPT Scholar, we explored their abilities to conduct a systematic literature review. Using as a specific domain, an attempt to frame/ methodize clinical assessment instruments used to evaluate neurofunctional deficits in Parkinson's disease (PD) -including framed through the ICF(-DH) paradigm - this paper is as well, a follow-up regarding the most actual subject matter of the AI's capabilities evolution in this respect. As well-known clinical-/paraclinical-/functional evaluations, using assessment quantitative instruments, are basic endeavors for rehabilitation, as they enable setting of appropriate and realistic therapeutic rehabilitative specific goals. Within the actual work, we have first achieved a narrative synthesis of the main molecular mechanisms involved in PD pathophysiology, underpinning its clinical appearance and evolution. To fundament our knowledge on an up-to-date information regarding the clinical-functional evaluation tools practiced in PD, we systematically reviewed the literature in this domain, PROSPERO registered ID: 593398, published in the last 6 years, through a PRISMA type method. The same keywords combinations/syntaxes have been used contextually, also to dialogize with ChatGPT4.o and ChatGPT Scholar Applying PRISMA (HI achieved), we have selected 24 articles. Interrogating the two AI above-mentioned models, we obtained quite difficult to be availed/ useful -comparative to our HI obtained -outcomes. Thus, when interrogating ChatGPT4.o, ChatGPT Scholar repeatedly, they provided -partially diverse -inappropriate related answers, including ones pending on the interrogator's IP, although they claimed to have this capacity. We consider, regarding their capabilities to achieve systematic literature reviews, that neither ChatGPT 4.o nor ChatGPT Scholar still cannot succeed this (yet, they keep improving lately). Additionally, we have consistently extended, including within a narrative related literature review, our "dialogue" with these two AI facilities regarding their availability to enhance the related evaluation instruments accuracy on neurofunctional assessments within biomarker-based frameworks. So, we aimed to emphasize the main topical data regarding these two important paradigms (based on HI and on AI) acquirements -considering the impetuous development of the latter -and thus, possibly to contribute inclusively at improving the actual performances to achieve Systematic Literature Reviews through the PRISMA type method -for the moment still better served by HI.