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In November 2022, OpenAI publicly launched its large language model (LLM), ChatGPT, and reached the milestone of having over 100 million users in only 2 months. LLMs have been shown to be useful in a myriad of health care–related tasks and processes. In this paper, I argue that attention to, public access to, and debate about LLMs have initiated a wave of products and services using generative artificial intelligence (AI), which had previously found it hard to attract physicians. This paper describes what AI tools have become available since the beginning of the ChatGPT revolution and contemplates how it they might change physicians’ perceptions about this breakthrough technology.

Physicians have been performing the art of medicine for hundreds of years, and since the ancient era, patients have turned to physicians for help, advice, and cures. When the fathers of medicine started writing down their experience, knowledge, and observations, treating medical conditions became a structured process, with textbooks and professors sharing their methods over generations. After evidence-based medicine was established as the new form of medical science, the art and science of medicine had to be connected. As a result, by the end of the 20th century, health care had become highly dependent on technology. From electronic medical records, telemedicine, three-dimensional printing, algorithms, and sensors, technology has started to influence medical decisions and the lives of patients. While digital health technologies might be considered a threat to the art of medicine, I argue that advanced technologies, such as artificial intelligence, will initiate the real era of the art of medicine. Through the use of reinforcement learning, artificial intelligence could become the stethoscope of the 21st century. If we embrace these tools, the real art of medicine will begin now with the era of artificial intelligence.

Analyzing the future of medicine and healthcare, especially during the rise of digital health and artificial intelligence, should rely on established futures methods that the discipline of futures studies has been using for decades. By employing such methods, healthcare professionals, policymakers and patient leaders could better navigate the complexities of modern healthcare, anticipate emerging challenges, and shape a future that is not just awaited but actively constructed.

Prompt engineering is a relatively new field of research that refers to the practice of designing, refining, and implementing prompts or instructions that guide the output of large language models (LLMs) to help in various tasks. With the emergence of LLMs, the most popular one being ChatGPT that has attracted the attention of over a 100 million users in only 2 months, artificial intelligence (AI), especially generative AI, has become accessible for the masses. This is an unprecedented paradigm shift not only because of the use of AI becoming more widespread but also due to the possible implications of LLMs in health care. As more patients and medical professionals use AI-based tools, LLMs being the most popular representatives of that group, it seems inevitable to address the challenge to improve this skill. This paper summarizes the current state of research about prompt engineering and, at the same time, aims at providing practical recommendations for the wide range of health care professionals to improve their interactions with LLMs.

When large language models (LLMs) were introduced to the public at large in late 2022 with ChatGPT (OpenAI), the interest was unprecedented, with more than 1 billion unique users within 90 days. Until the introduction of Generative Pre-trained Transformer 4 (GPT-4) in March 2023, these LLMs only contained a single mode—text. As medicine is a multimodal discipline, the potential future versions of LLMs that can handle multimodality—meaning that they could interpret and generate not only text but also images, videos, sound, and even comprehensive documents—can be conceptualized as a significant evolution in the field of artificial intelligence (AI). This paper zooms in on the new potential of generative AI, a new form of AI that also includes tools such as LLMs, through the achievement of multimodal inputs of text, images, and speech on health care’s future. We present several futuristic scenarios to illustrate the potential path forward as multimodal LLMs (M-LLMs) could represent the gateway between health care professionals and using AI for medical purposes. It is important to point out, though, that despite the unprecedented potential of generative AI in the form of M-LLMs, the human touch in medicine remains irreplaceable. AI should be seen as a tool that can augment health care professionals rather than replace them. It is also important to consider the human aspects of health care—empathy, understanding, and the doctor-patient relationship—when deploying AI.

The rapid progress in the development of artificial intelligence (AI) is having a substantial impact on health care (HC) delivery and the physician-patient interaction. This scoping review aims to offer a thorough analysis of the current status of integrating AI into medical practice as well as the apprehensions expressed by HC professionals (HCPs) over its application. This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to examine articles that investigated the apprehensions of HCPs about medical AI. Following the application of inclusion and exclusion criteria, 32 of an initial 217 studies (14.7%) were selected for the final analysis. We aimed to develop an attitude range that accurately captured the unfavorable emotions of HCPs toward medical AI. We achieved this by selecting attitudes and ranking them on a scale that represented the degree of aversion, ranging from mild skepticism to intense fear. The ultimate depiction of the scale was as follows: skepticism, reluctance, anxiety, resistance, and fear. In total, 3 themes were identified through the process of thematic analysis. National surveys performed among HCPs aimed to comprehensively analyze their current emotions, worries, and attitudes regarding the integration of AI in the medical industry. Research on technostress primarily focused on the psychological dimensions of adopting AI, examining the emotional reactions, fears, and difficulties experienced by HCPs when they encountered AI-powered technology. The high-level perspective category included studies that took a broad and comprehensive approach to evaluating overarching themes, trends, and implications related to the integration of AI technology in HC. We discovered 15 sources of attitudes, which we classified into 2 distinct groups: intrinsic and extrinsic. The intrinsic group focused on HCPs' inherent professional identity, encompassing their tasks and capacities. Conversely, the extrinsic group pertained to their patients and the influence of AI on patient care. Next, we examined the shared themes and made suggestions to potentially tackle the problems discovered. Ultimately, we analyzed the results in relation to the attitude scale, assessing the degree to which each attitude was portrayed. The solution to addressing resistance toward medical AI appears to be centered on comprehensive education, the implementation of suitable legislation, and the delineation of roles. Addressing these issues may foster acceptance and optimize AI integration, enhancing HC delivery while maintaining ethical standards. Due to the current prominence and extensive research on regulation, we suggest that further research could be dedicated to education.

Digitalisation affects 90% of healthcare. Digital health, however, does not only refer to technological transformation but also has considerable cultural and social consequences. It fundamentally reshapes the roles of physicians and patients, as well as their relationship. Moreover, from the second half of the 20th century, the growing number of chronic patients and the increase in life expectancy have posed new challenges to the medical workforce. To explore the digitally engaged physician's knowledge and attitudes towards digital health technologies and the transformation of the doctor-patient relationship. A qualitative interview study analysed with Interpretative Phenomenological Analysis (IPA). The study is based on qualitative, semi-structured interviews with 11 digitally engaged physicians from 9 countries. We identified four main themes emerging from e-physicians' responses and experience: 1) the past: intentions and experiences of change, 2) the present: the role of digital health and technology in the medical practice and their everyday challenges, 3) the present: the practical and ideal physician-patient relationship, and 4) the future: skills and competencies needed for working with e-patients and visions about the future of the medical practice. The interviewed physicians state that digital health solutions could create a deeper doctor-patient relationship: knowledgeable patients are a huge help in the joint work effort and technology is the main tool for creating a more involved and responsible patient. Medical professionals in the future might rather get a role as a translator between technical data and the patient; as a guide in the jungle of digital health. However, the interviewed physicians also noted that digital transition today is more beneficial to patients than to their doctors. We state that digitally engaged physicians are characterized by a kind of dichotomy: they use digital opportunities enthusiastically, but they also feel the difficulties related to digital health.

Primary health care (PHC) is critical for delivering accessible and continuous care but faces persistent challenges such as workforce shortages, administrative burden, and rising multimorbidity. Artificial intelligence (AI) has the potential to support PHC by enhancing diagnosis, workflow efficiency, and clinical decision-making. However, existing research often overlooks how AI tools function within the complex realities of primary care and how clinicians and patients experience them. This scoping review maps the landscape of AI applications in PHC, with a focus on empirical studies involving direct engagement from PHC stakeholders. The review emphasizes real-world settings, clinical workflows, and the alignment of AI tools with the values and complexity of generalist care. Following Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we searched PubMed, Web of Science, and Scopus databases up to April 13, 2024. Inclusion criteria were empirical, peer-reviewed studies published in English between January 2010 and April 2024, involving direct stakeholder interaction (general practitioners, nurses, or patients) in real-world PHC settings, evaluating AI applications (eg, diagnostics, workflow optimization, and documentation). Exclusions included algorithm-only validations, pediatric populations, secondary or tertiary care contexts not explicitly addressing PHC workflows, nonempirical research (eg, editorials or protocols), and non-English studies. We used thematic analysis to synthesize findings related to study aims, AI applications, and stakeholder roles. Of 5224 identified records, 73 studies met the inclusion criteria. Studies were grouped into four main themes: (1) early intervention and decision support (n=21; 29%), (2) chronic disease management (n=16; 22%), (3) operations and patient management (n=12; 16%), and (4) acceptance and implementation experiences (n=24; 33%). AI tools frequently demonstrated strong technical accuracy, particularly in diagnostic decision support. However, implementation in routine practice was often limited by usability barriers, workflow misalignment, trust concerns, equity gaps, and financial constraints. Overall, AI holds significant potential to support PHC, especially when aligned with clinical reasoning, workflow needs, and relational care models. However, persistent implementation barriers such as usability challenges, training gaps, and workflow integration issues must be addressed. The evidence included in this review is limited by heterogeneity in study design and the predominance of small-scale feasibility studies. Future research should prioritize pragmatic trials, co-design with PHC professionals, and anticipatory planning using future methods to ensure responsible and equitable implementation.

In the 21st century, health care has been going through a paradigm shift called digital health. Due to major advances and breakthroughs in information technologies, most recently artificial intelligence, the patriarchy of the doctor-patient relationship has started evolving toward an equal-level partnership with initial signs of patient autonomy. Being an underused resource for centuries, patients have started to contribute to their care with information, data, insights, preferences, and knowledge. It is important to recognize that at its core, digital health represents a cultural transformation, where patient empowerment has likely played the most significant role in driving these changes. This viewpoint paper traces the remarkable journey of patient empowerment from its nascent stages to its current prominence in shaping health care’s future. Spanning over two and a half decades, we explore pivotal moments and technological advancements that have revolutionized the patient’s role in health care. We dive into a few historical milestones, mainly in the United States, that have challenged and redefined societal norms around agency, drawing parallels between patient empowerment and broader social movements, such as the women’s suffrage and civil rights movements. Through these lenses, we argue that patient empowerment is not solely a function of knowledge or technology but requires a fundamental shift in societal attitudes, policies, health care culture, and practices. As we look to the future, we posit that the continued empowerment of patients will play a pivotal role in the development of more equitable, effective, and personalized health care systems. This paper calls for an ongoing commitment to fostering environments that support patient agency, access to resources, and the realization of patient potential in navigating and contributing to their health outcomes with an emphasis on the emerging significance of patient design.

At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.