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.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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.
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.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Artificial intelligence (A.I.) is expected to significantly influence the practice of medicine and the delivery of healthcare in the near future. While there are only a handful of practical examples ...for its medical use with enough evidence, hype and attention around the topic are significant. There are so many papers, conference talks, misleading news headlines and study interpretations that a short and visual guide any medical professional can refer back to in their professional life might be useful. For this, it is critical that physicians understand the basics of the technology so they can see beyond the hype, evaluate A.I.-based studies and clinical validation; as well as acknowledge the limitations and opportunities of A.I. This paper aims to serve as a short, visual and digestible repository of information and details every physician might need to know in the age of A.I. We describe the simple definition of A.I., its levels, its methods, the differences between the methods with medical examples, the potential benefits, dangers, challenges of A.I., as well as attempt to provide a futuristic vision about using it in an everyday medical practice.
A paradigm shift is underway in the patient-clinician relationship, driven by irreversible changes in information access, yet the model under which clinicians are trained, care is conducted, and care ...delivery is designed has not changed significantly even though we call it “patient centered.” Humanity endured centuries in which even doctors had little idea what the patient’s problem really was. Science slowly solved that, and for a century, only doctors could know what was worth knowing. Today, the rise of the internet and digital health has led to the end of that era. We are already witnessing early signs of the era of participatory health: genuinely empowered people living their lives and managing their health according to their own priorities, in partnership and consultation with physicians as needed. This may feel like a threat to the physician’s sacred role, but it is no more so than when physicians adopted informed consent and then shared decision-making. In the 2010s, many pharmaceutical, medical, and health care companies started to use patient centricity as a mantra. We argue that to drive this paradigm change fully into existence, we need to shift “patient centricity” from a relatively passive process, driven by industry needs, into a far more active, collaborative process driven by both parties’ needs and preferences. To build this new world of practice and workflow, we simply must engage with patients as true partners. To achieve medicine’s new potential, it must be optimized around the wants and priorities of the ultimate stakeholder—the party that has the most at stake in how it all plays out: the patient. Patient design is the approach that can make it happen.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The rapid advancements in artificial intelligence (AI) have led to the development of sophisticated large language models (LLMs) such as GPT-4 and Bard. The potential implementation of LLMs in ...healthcare settings has already garnered considerable attention because of their diverse applications that include facilitating clinical documentation, obtaining insurance pre-authorization, summarizing research papers, or working as a chatbot to answer questions for patients about their specific data and concerns. While offering transformative potential, LLMs warrant a very cautious approach since these models are trained differently from AI-based medical technologies that are regulated already, especially within the critical context of caring for patients. The newest version, GPT-4, that was released in March, 2023, brings the potentials of this technology to support multiple medical tasks; and risks from mishandling results it provides to varying reliability to a new level. Besides being an advanced LLM, it will be able to read texts on images and analyze the context of those images. The regulation of GPT-4 and generative AI in medicine and healthcare without damaging their exciting and transformative potential is a timely and critical challenge to ensure safety, maintain ethical standards, and protect patient privacy. We argue that regulatory oversight should assure medical professionals and patients can use LLMs without causing harm or compromising their data or privacy. This paper summarizes our practical recommendations for what we can expect from regulators to bring this vision to reality.
Artificial intelligence (AI) has the potential to ease the human resources crisis in healthcare by facilitating diagnostics, decision-making, big data analytics and administration, among others. For ...this we must first tackle the technological, ethical and legal obstacles.The human resource crisis is widening worldwide, and it is obvious that it is not possible to provide care without workforce. How can disruptive technologies in healthcare help solve the variety of human resource problems? Will technology empower physicians or replace them? How can the medical curriculum, including post-graduate education prepare professionals for the meaningful use of technology? These questions have been growing for decades, and the promise of disruptive technologies filling them is imminent with digital health becoming widespread. Authors of this essay argue that AI might not only fill the human resources gap, but also raises ethical questions we need to deal with today.While there are even more questions to address, our stand is that AI is not meant to replace caregivers, but those who use AI will probably replace those who don't. And it is possible to prepare for that.
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CEKLJ, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Physicians have been taking the Hippocratic Oath for centuries. The Oath contains a set of ethical rules designed to guide physicians through their profession; it articulates a set of true north ...principles that govern the practice of medicine. The Hippocratic Oath has undergone several revisions, most notably in 1948 by the World Medical Association. However, in an era of rapid change in medicine, we believe it is time to update the Oath with modest but meaningful additions so that it optimally reflects 21st century health care. The rise of digital health has dramatically changed the practice of medicine in a way that could not have been easily predicted at the time Hippocrates outlined his ethical principles of medicine. Digital health is a broad term that encompasses use of digital devices and platforms, including electronic health records, patient-provider portals, mobile health apps, wearable biosensors, artificial intelligence, social media platforms, and medical extended reality, to improve the process and outcomes of health care delivery. These technologies have driven a cultural transformation in the delivery of care. We offer modest suggestions to help prompt discussion and contemplation about the current Oath and its relevancy to our changing times. Our suggestions are not meant to be a definitive set of final recommendations. Rather, we propose new text that bodies such as the World Medical Association might consider integrating into an updated Oath, just as previous changes were adopted to ensure the Oath remains relevant and impactful for all physicians and their patients.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Health care in the 21st century has started undergoing major changes due to the rising number of patients with chronic conditions; increased access to new technologies, medical information, and peer ...support via the internet; and the ivory tower of medicine breaking down. This marks the beginning of a cultural transformation called digital health. This has also led to a shift in the roles of patients and medical professionals, resulting in a new, equal partnership. When COVID-19 hit, the adoption of digital health technologies skyrocketed. The technological revolution we had been aiming for in health care took place in just months due to the pandemic, but the cultural transition is lagging. This creates a dangerous gap between what is possible technologically through remote care, at-home lab tests, or health sensors and what patients and physicians are actually longing for. If we do it well enough now, we can spare a decade of technological transformations and bring that long-term vision of patients becoming the point of care to the practical reality of today. This is a historic opportunity we might not want to waste.
As digital health technologies make patients the point of care, The Medical Futurist and its accompanying Institute are designed to help individual patients and medical professionals prepare for ...changes in the near future.
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