With the Specialist LLC’s Post

Exciting advancements in AI research are paving the way for large multimodal models (LMMs), combining text, images, and more for enhanced capabilities. From assisting the visually impaired to revolutionizing industries like healthcare and e-commerce, the potential of multimodal AI is vast. With the ability to process diverse data types, LMMs offer a more human-like approach to problem-solving and decision-making. As this technology continues to evolve, the possibilities for innovation and transformation are endless. #MultimodalAI #LMMs #AI #Innovation #Technology #ArtificialIntelligence https://lnkd.in/gxUbq2tE

Beyond Symptoms: The Complex Path of AI in Healthcare   Artificial Intelligence (AI) is transforming various sectors, from agriculture to finance, and its impact on healthcare is profound. AI’s potential to revolutionize how we diagnose, treat, and manage health is immense. Already, AI plays a key role in assisting medical professionals, from improving diagnostics to managing patient records. Machine learning algorithms can identify patterns in large datasets, enhancing decision-making and supporting early detection of diseases like cancer or neurological conditions by analyzing imaging scans.   A major benefit of AI is its ability to address medical personnel shortages, particularly in underserved areas. AI-driven diagnostic tools and self-service health applications can help patients assess symptoms and receive preliminary recommendations. These AI tools can simulate consultations by gathering patient information, asking relevant questions, and offering initial diagnoses, providing a valuable resource where access to healthcare is limited.   In my own experience working with AI in healthcare, I tested large language models (LLMs) for diagnostic purposes. In one case, involving pregnancy symptoms, the LLM effectively asked questions and provided an accurate preliminary diagnosis, showing the potential of AI in supporting healthcare professionals in making informed decisions.   However, despite these promising applications, significant challenges remain, particularly in mental health. While physical health issues can be approached with data-driven methods, mental health requires understanding both the conscious and unconscious mind. Therapists use nuanced human interactions to interpret non-verbal cues and guide patients through emotions. Although AI can analyze language patterns, behavioral data, and even brain activity, it cannot fully decipher the complexities of the human psyche. While AI can provide insights into unconscious signals, it cannot replace the empathy and intuition required for comprehensive mental health care.   The integration of AI in healthcare represents an exciting frontier. Despite the limitations, particularly in understanding unconscious processes, the advancements made so far suggest that overcoming these challenges is possible. Just as mapping the human genome seemed impossible at one time, AI breakthroughs in healthcare may follow a similar trajectory. Ongoing collaboration between AI researchers, healthcare professionals, and technologists will be essential for progress.   What are your thoughts on AI's potential to reshape healthcare? Share your perspective in the comments, and let’s continue this important conversation. Feel free to inbox me.   #AI_in_healthcare #medical_technology #machine_learning #mental_health #innovation_in_healthcare #LLMs #future_of_medicine

If health is a fundamental human right, health-care delivery must be improved globally to achieve universal access and #artificialintelligence #ai is poised to fill a gap.

It's odd how quickly AI is becoming the Bogeyman in healthcare. However, very few industries fight tooth and nail against change/innovation like healthcare does so I guess it's just par for the course. #healthcare #innovation #ai #generativeai #chatgpt #llm #leadership #change https://lnkd.in/gxWmgEMT

The integration of AI in healthcare holds immense potential, yet building trust remains a pivotal challenge. Transparency and validation are paramount, with a McKinsey report revealing that 71% of respondents deem a company's AI policy crucial in decision-making. Traditional trust in healthcare relies on a robust framework of regulations, posing a challenge for AI systems lacking explainability in their responses. Validation processes, akin to those for medical professionals, are essential to ensure AI consistently delivers accurate and explainable results. New laws and regulations are imperative in the AI domain, especially in healthcare, where people's lives are at stake. Incremental trust-building involves a phased implementation strategy, allowing medical professionals to retain control while AI supports tasks and offers insights. Crucially, the delicate patient-physician relationship, rooted in empathy and expertise, faces potential erosion with AI. Balancing innovation and compassion is key to seamlessly integrating technology while preserving trust. The journey to trust in healthcare AI involves rigorous validation, unwavering transparency, gradual implementation, and a commitment to maintaining the human touch, demonstrating that AI is a reliable ally in the evolving landscape of healthcare. #aiinhealthcare #trust With the Specialist combines artificial intelligence with a board certified neurologist to enhance neurological care in the primary care setting. #withthespecialist #neurology https://lnkd.in/g6jNXz2y https://lnkd.in/gExrwg2P

Human bias awareness in healthcare is unfortunately a very real, persistent issue. As healthcare increasingly adopts AI, many patients remain hesitant due to a preference for the human touch in the field. Studies have shown, however, that patients may be more open to AI implementation if it eliminates currently existing biases. Highlighting bias awareness in healthcare could help promote greater trust in AI-driven medicine. #Healthcare #AI #HealthEquity

What is our human competitive advantage versus #AI during an organizational #workforce decision-making process? 🎭 Our competitive advantage depends on our creativity 🎨, social skills 🧔 (associated with empathy and connection), and analytical prowess 🧠. Even though #AI can generate striking #data, the success of #research processes and #project implementations hinges on our ability to understand and sort out the information, contextualize results, and create customized #solutions. Therefore, what happens when we lack our hallmark?... when we find it difficult to devise new things, feel uncomfortable #connecting with others, and don´t like to drill down on #data... We lose the only part that steps up our #performance and differentiates us from #AI, and consequently, we become replaceable. It´s an undeniable truth. Even when the research needs more parameters to hone its point and back its results, some article assertions aren´t far from reality, especially when we talk about service careers 🏥 . Here is the complete article: https://lnkd.in/eh4w-tNd

Louis Leakey's response to Jane Goodall's observations about chimpanzees using tools invites us to rethink our own categories and definitions, much like the advent of AI in almost all domains including medicine is doing today. As AI starts to improve and excel at tasks ranging from diagnostics to personalized treatment plans—areas traditionally held by humans—it prompts a reevaluation of the traditional methods we use in healthcare and the roles within it. This technological shift is fundamentally altering how we think about the interaction between technology and human expertise. Just as recognizing chimpanzees' tool use expanded our understanding of intelligence and tool usage beyond humans, increasing integration of AI into healthcare is expanding our understanding of medical practice, enhancing our capabilities and reshaping our approach to patient care, mirroring the transformative impact that recognizing chimpanzees' tool use had on anthropology. For healthcare professionals yet to explore the field of AI, now is the perfect moment to stop pushing back and begin learning. Embrace the opportunity to understand AI technologies, grasp their applications, and contribute your valuable insights. By engaging with and helping shape Med AI, you ensure it supports your work and enhances patient care, rather than becoming another fishbone in the throat! Instead of seeing AI as a challenge to traditional methods, let’s recognize it as a tool that amplifies your capabilities and helps enhance patient outcomes. Don’t sit on the sidelines; dive into this journey to become a leader in the next wave of medical innovation! Please feel free to reach out to me directly for guidance and support in your Med AI journey. And if you haven't already, make sure to join (and share with your peers) the Doctors AI LinkedIn Group to stay updated and connect with like minded colleagues - LINK IN COMMENTS 👇 Photo By David Rodish/Cronkite News

🚀 The Future of Artificial Intelligence in Healthcare The article explores how AI can transform healthcare through diagnostics, personalized treatment, monitoring, administrative tasks, and drug discovery. Read the full blog post by Amit Kumar at https://lnkd.in/eRum_PNM #AI #Healthcare #Diagnostics #DrugDiscovery #PatientCare #Codedash

AI and Life-and-Death Decisions: A Firm No, but Recognizing Useful Benefits Full article The integration of artificial intelligence (AI) into critical sectors has sparked debate, particularly regarding life-and-death decisions in healthcare, criminal justice, and end-of-life care. While AI can offer valuable benefits, it’s crucial to reject its role in these sensitive areas. Here’s why. The Case Against AI in Life-and-Death Decisions 1. Lack of Human Empathy: AI cannot replicate the empathy and compassion essential for life-and-death choices. Such decisions demand human judgment that AI cannot provide. 2. Ethical Considerations: AI operates on algorithms that may overlook ethical complexities. In healthcare, a data-driven approach can prioritize efficiency over patient well-being, disregarding individual values and dignity. 3. Accountability Issues: AI’s involvement raises questions about responsibility. If an AI system fails, who is accountable? This ambiguity can create a dangerous environment regarding life-and-death outcomes. 4. Bias and Discrimination: AI systems trained on historical data may perpetuate biases, leading to discriminatory practices in critical areas, exacerbating inequalities instead of alleviating them. 5. Complexity of End-of-Life Decisions: These decisions involve medical, ethical, and emotional considerations. AI lacks the nuance to navigate these complexities, undermining the personal nature of end-of-life care. Recognizing the Useful Benefits of AI While rejecting AI in critical decisions, we can acknowledge its supportive role: 1. Data Analysis: AI can quickly analyze vast datasets, enhancing diagnostic accuracy and informing human decisions in healthcare. 2. Predictive Modeling: AI can forecast outcomes, helping professionals allocate resources effectively in public health. 3. Decision Support: AI can recommend actions based on data, but final decisions should remain with human caregivers. 4. Training and Simulation: AI can enhance training for medical professionals, improving their ability to make tough choices. Conclusion While AI has useful applications in enhancing decision-making, it must not be allowed to make life-and-death decisions. The risks of lacking empathy, ethical understanding, and accountability highlight the need to keep these responsibilities firmly in human hands. AI should support, not replace, human judgment. References: https://lnkd.in/gxXM6G2A https://lnkd.in/g67FG6hv https://lnkd.in/gBgjAnZr #ai #endoflifedecisions