Giuseppe Comerci

ChatGPT Health, an artificial intelligence feature launched by OpenAI in January 2026, integrates personal medical records and consumer health data into a large language model–based chatbot. It positions itself as a digital health tool to help users navigate a fragmented US health care system marked by inaccessible insurance, medical deserts, and workforce shortages. We argue that, rather than closing these gaps, ChatGPT Health risks widening what we call the solidarity gap in health care. By appearing to address unmet needs, it may obscure and entrench the structural conditions that produce health care disparities. We identify specific risks to patient safety, such as the legitimization of dangerous self-rationing and self-medication behaviors, inaccurate triage of clinical emergencies, erosion of effective health communication, and the reinforcement of confirmation and anchoring bias. These risks are particularly acute for individuals facing structural barriers to care. To prevent ChatGPT Health and similar large language model–based health tools from deepening inequities, we propose solidarity-based policy interventions such as upstream reforms that rebuild the institutional foundations of access and health equity and downstream safeguards that embed artificial intelligence tools within accountable, equitable health care infrastructures as complements to rather than substitutes for human care.

The creation of virtual brains has long attracted the attention of the scientific community. Over time, various projects have aimed to produce brain simulations of varying complexity. These scientific efforts have often been accompanied by lively philosophical debates highlighting the ethical implications of brain simulations, such as their moral status, the possibility of developing consciousness and personhood, and concerns about unethical experimentation. In particular, the possibility that a highly complex virtual brain could experience pain has been a central concern, especially in the context of the Human Brain Project. This paper presents a conceptual analysis that traces the legacy of this debate and situates it within the current scientific context. The issue warrants renewed attention for two reasons. First, the literature has generally treated pain as an abstract concept, without situating it within a specific theoretical framework. Second, recent technologies for medical purposes, such as digital brain twins, promise to connect virtual brains with their biological brains, offering new perspectives on the question of pain. Adopting a mixed conception of pain that integrates the sensory and affective dimensions, two requirements for experiencing pain can be extrapolated. These requirements are then implemented in a comparative conceptual analysis to examine three different virtual brain architectures. The findings suggest that, from a theoretical standpoint, highly advanced virtual brains connected in real time to their biological counterparts could experience psychological pain.

Unresponsive Wakefulness Syndrome (UWS), a condition characterized by wakefulness without awareness, presents significant medical and moral uncertainties, particularly in end-of-life decision-making. Digital Brain Twins (DBTs), virtual replicas of patients’ brains driven by advanced artificial intelligence, offer the potential to alleviate medical uncertainties by providing precise diagnoses, prognoses, and experimental platforms for treatment testing. This paper provides a theoretical contribution by examining the potential ethical impact of these technologies in the context of UWS. We argue that, while DBTs promise greater diagnostic accuracy, personalized predictions of recovery, and non-invasive tools for exploring therapeutic interventions, they do not necessarily resolve the moral uncertainties faced by proxy decision-makers. Decisions about withdrawing or continuing life-sustaining treatment are fundamentally moral and value-laden, often extending beyond empirical evidence provided by DBTs. Factors such as cognitive biases, emotional distress, and subjective interpretations of best interest further complicate these decisions. While DBTs represent a breakthrough in precision medicine, their role in navigating the ethical complexities of UWS is limited, emphasizing the need for integrated approaches that combine technological innovation with ethical and psychological support for proxies.

_Riassunto_: La _Deep Brain Stimulation _(DBS) è un dispositivo annoverato tra le interfacce-cervello computer e che si qualifica come una promettente soluzione medica per far fronte al decorso di alcune malattie neurodegenerative come il Parikinson. Decenni di utilizzo clinico della DBS hanno permesso di comprenderne gli effetti collaterali e il loro impatto sul PIAAAS (_Personality, Identity, Agency, Authenticity, Autonomy and Self_). In tal senso, per sondare cambiamenti psicologici legati all’uso della DBS si è fatto ricorso a metodi quantitativi, come questionari incentrati su valutazioni di ordine numerico, e metodi qualitativi, che si appoggiano alle esperienze riportate dai pazienti tramite interviste. Questo articolo ha l’obiettivo di evidenziare come, a prescindere dai metodi di indagine utilizzati, la ricerca sia stata sempre focalizzata sugli effetti collaterali della DBS e gli eventuali cambiamenti sulle dimensioni comprese nel PIAAAS, lasciandosi sfuggire l’occasione di usare i numerosi dati raccolti per effettuare un’analisi fenomenologica che potesse restituirci una descrizione della sintesi uomo-macchina. Per colmare questo iato, proponiamo l’utilizzo sinergico di due metodi di raccolta e analisi dati di origine fenomenologica. _Parole chiave_: Deep Brain Stimulation; Effetti collaterali; Sintesi uomo-macchina; Fenomenologia _Deep Brain Stimulation and the human-machine synthesis: A possible phenomenological perspective_ _Abstract_: Deep Brain Stimulation (DBS) is a device included among the brain-computer interfaces and that qualifies as a promising medical solution to cope with the course of some neurodegenerative diseases such as Parikinson’s. Decades of clinical use of DBS have allowed us to understand its side effects and their impact on PIAAAS (_Personality, Identity, Agency, Authenticity, Autonomy and Self_). In this sense, to investigate psychological changes related to the use of DBS, quantitative methods, such as questionnaires focused on numerical assessments, and qualitative techniques, which are based on experiences by patients through interviews, have been used. This article aims to highlight how, regardless of the survey methods used, research has always been focused on the side effects of DBS and possible changes in the concepts included in the PIAAAS, missing the opportunity to use the numerous data collected to carry out a phenomenological analysis that could give us a description of the human-machine synthesis. To bridge this hiatus, we propose the synergistic use of two phenomenological methods of data collection and analysis. _Keywords_: Deep Brain Stimulation; Side Effects; Human-machine synthesis; Phenomenology.