Risks and benefits of an AI revolution in medicine Harvard Gazette

Et al. (2020), advances in surgery have revolutionized the management of both acute and chronic diseases, prolonging life and extending the boundary of patient survival [62]. Moreover, current robots can already automatically perform some simple surgical tasks, such as suturing and knot tying [63,64]. For example, in 2016, a smart surgical robot stitched up a pig’s small intestines completely on its own and was able to outperform human surgeons who were given the same task [65]. These selected studies provided valuable insights into the use and impact of AI in various medical specialties, forming the basis of our review. Briefly and very simply (Fig. 2.3

), the act of convolving an image with various weights and creating a stack of filtered images is referred to as a convolutional layer, where an image essentially becomes a stack of filtered images. Pooling is then applied to all these filtered images, where the original stack of images becomes a smaller representation of themselves and all negative values are removed by a rectified linear unit (ReLU).

Shaban et al. (2019) trained a novel CNN system to quantify TILs from WSIs of oral squamous cell carcinomas and achieved an accuracy of 96% [75]. Et al. conducted a study in 2019 which concluded that a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses [76]. There is an ongoing transformation within surgical technology and focus has especially been placed in reducing the invasiveness of surgical procedure by minimizing incisions, reducing open surgeries, and using flexible tools and cameras to assist the surgery [42].

3.3. Augmented reality and virtual reality in the healthcare space

Earlier this year, four federal agencies signed on to a joint statement that they will more closely scrutinize discriminatory uses of and bias in AI practices. Department of Health and Human Services, indicating heightened scrutiny of the use of pixel tech, biometric data and other facets of AI-dependent healthcare websites and apps. The Federal Trade Commission’s recent enforcement actions against healthcare organizations likewise signify close attention to this space. As AI moves into more areas of health care, many ethical issues will need to be addressed, explains Kontos. The field of AI is continuously evolving and researchers are exploring various avenues to create intelligent systems with different capabilities. The authors employed a visual representation, in the form of Figure 1, to illustrate the diverse subtypes of AI.

Gen-AI-enabled technology could also streamline health insurance prior authorization and claims processing, two time-intensive and costly tasks for private payers. Gen-AI technology relies on deep-learning algorithms to create new content such as text, audio, code, and more. These unstructured data sets can be used independently or combined with large, structured data sets, such as insurance claims. Overall, this report highlights the excitement of Europe-wide stakeholders, healthcare professionals, investors, and innovators about the impact of AI on European healthcare, and about the thoughtful approach taken across Europe to ensure this delivers ethical and trustworthy AI. It also highlights that this is only the latest view across Europe and internationally—speed is of the essence if Europe is to continue playing a leading role in shaping the AI of the future to deliver its true potential to European health systems and their patients.

Doctors, researchers, and entrepreneurs alike have been focused on how AI can improve the health care system. AI-enabled health startups raised nearly $10B in funding in 2021 and more than $3B in the first half of 2022. Yarid, who currently leads the AI task force for the Educational Council on Osteopathic Principles (ECOP) and is also spearheading efforts to incorporate AI into medical education at PCOM, believes the technology can dramatically improve the practice of medicine. Bringing these fields together to better understand how AIs work once they’re “in the wild” is the mission of what Parkes sees as a new discipline of machine behavior. Computer scientists and health care experts should seek lessons from sociologists, psychologists, and cognitive behaviorists in answering questions about whether an AI-driven system is working as planned, he said. “I’m very excited about this team aspect and really thinking about the things that AI and machine-learning tools can provide an ultimate decision-maker — we’ve focused on doctors so far, but it could also be the patient — to empower them to make better decisions,” Doshi-Velez said.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

They use AI in every step of their drug discovery and development process including target discovery, lead optimization, toxicity assessment, and innovative trial design. We hold the view that AI amplifies and augments, rather than replaces, human intelligence. Hence, when building AI systems in healthcare, it is key to not replace the important elements of the human interaction in medicine but to focus it, and improve the efficiency and effectiveness of that interaction. Moreover, AI innovations in healthcare will come through an in-depth, human-centred understanding of the complexity of patient journeys and care pathways.

Such minimally invasive surgery is seen as the way forward, but it is still in an early phase with many improvements to be made to make it “less of a big deal” for patients and reduce time and cost. Minimal invasive surgery requires different motor skills compared with conventional surgery due to the lower tactile feedback when relying more on tools and less on direct touching. Sensors that provide the surgeon with finer tactile stimuli are under development and make use of tactile data processing to translate the sensor input into data or stimuli that can be perceived by the surgeon.

Artificial intelligence in healthcare: transforming the practice of medicine

Gen-AI models can summarize denial letters, consolidate denial codes, highlight relevant denial reasons, and contextualize and provide next steps for denials management, although all of this would still need to be conducted under human supervision. Consumers are demanding more personalized and convenient services from their health insurance. At the same time, private payers face increasing competitive pressure and rising healthcare costs. Gen AI can help private payers’ operations perform more efficiently while also providing better service to patients and customers. In machine learning, a computer model is built to predict what may happen in the real world.

By analyzing thousands of cells in minutes, the app revolutionized hematology and cell morphology, enabling the early detection of hematological-based diseases like cancers, infections or anemia. The early detection of these diseases improves the patient’s chances of recovery and improves the quality of their life. AI faces several challenges in healthcare, including the need for high-quality data, data interoperability, and ensuring AI systems are transparent and explainable. These issues are actively being addressed by the healthcare community, researchers, and regulatory bodies. While the term “artificial intelligence” might conjure images of futuristic robots, the field is actually the new frontier of health care. We can expect improvements and new applications as this amazing technology continues to advance with time.

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. Since the introduction of EMRs, there have been large databases of information on each patient, which collectively can be used to identify healthcare trends within different disease areas.

By changing a few pixels of an image of a cat — still clearly a cat to human eyes — MIT students prompted Google image software to identify it, with 100 percent certainty, as guacamole. Further, a well-known study by researchers at MIT and Stanford showed that three commercial facial-recognition programs had both gender and skin-type biases. In this article, we outline the emerging gen-AI use cases for private payers, hospitals, and physician groups. Many healthcare organizations are more likely to start with applying gen AI to administrative and operational use cases, given their relative feasibility and lower risk. Over time, once they have more experience and confidence in the technology, these organizations may start to use gen AI with clinical applications. Artificial intelligence is increasingly being used as a virtual tool in many countries around the world.

• Et al. (2022), within the last two decades, AI began to incorporate neuroimaging studies of psychiatric patients with deep learning models to classify patients with psychiatric disorders [53].
• Deep Genomics, a Healthtech company, is looking at identifying patterns in the vast genetic dataset as well as EMRs, in order to link the two with regard to disease markers.
• It is believed that within the next decade a large part of the global population will be offered full genome sequencing either at birth or in adult life.
• Overall, there is a significant opportunity for EU health systems, but AI’s full potential remains to be explored and the impact on the ground remains limited.
• Therapeutic drug monitoring (TDM) is a process used to optimize drug dosing in individual patients.

AI-powered data management systems seamlessly store and organize large amounts of data to draw meaningful conclusions and predictions. Companies should position themselves for success and leverage the independent review process to demonstrate robust health privacy and AI practices to the marketplace. BBB National Programs serves as a unifying voice that allows businesses and healthcare organizations to signal to consumers and regulators that they have taken steps to use AI responsibly. In line with consumer complaints and heightened patient angst about new tech, regulatory bodies are paying close attention to the use of AI, particularly the harm it could cause and how laws can address that harm.

Such tactile data processing typically makes use of AI, more specifically artificial neural networks to enhance the function of this signal translation and the interpretation of the tactile information [43]. Artificial tactile sensing compared with physical touching including a larger reference library to compare sensation and standardization among surgeons with respect to quantitative features, continuous improvement, and level of training. It uses a deep dynamic memory neural network to read and store experiences and in memory cells. The long short-term memory of the system models the illness trajectory and healthcare processes of users via a time-stamped sequence of events and in this way allows capturing long-term dependencies [41].

• The company takes a “federated learning” approach to data, meaning it partners with organizations to access their data and applies machine-learning algorithms to learn about diseases.
• At the time of writing (Early 2020), the threat of a SARS-COV-2 epidemic looms over many countries and is expanding at an unprecedented rate.
• Diseases such as sickle cell anemia, cystic fibrosis and Tay-Sachs disease are caused by errors in the order of DNA letters that codify the operating instructions for every human cell.
• The system was designed to show a set of reference images most similar to the CT scan it analyzed, allowing a human doctor to review and check the reasoning.

Interpretation of data that appears in the form of either an image or a video can be a challenging task. Experts in the field have to train for many years to attain the ability to discern medical phenomena and on top of that have to actively learn new content as more research and information presents itself. However, the demand is ever increasing and there is a significant shortage of experts in the field. There is therefore a need for a fresh approach and AI promises to be the tool to be used to fill this demand gap. Machine learning has also been implemented to assess the toxicity of molecules, for instance, using DeepTox, a DL-based model for evaluating the toxic effects of compounds based on a dataset containing many drug molecules [19]. Another platform called MoleculeNet is also used to translate two-dimensional molecular structures into novel features/descriptors, which can then be used in predicting toxicity of the given molecule.

Support Stanford Medicine

GPT-4, the large language model (LLM) underlying the most advanced version of the chatbot1, and others, such as Google’s Med-PaLM2, are poised to transform health care. The first stage is to design and develop AI solutions for the right problems using a human-centred AI and experimentation approach and engaging appropriate stakeholders, especially the healthcare users themselves. In 1956, John McCarthy organized the Dartmouth Conference, where he coined the term “Artificial Intelligence.“ This event marked the beginning of the modern AI era. Dr. Liz Kwo is chief commercial officer of Everly Health and a serial healthcare entrepreneur, physician and Harvard Medical School faculty lecturer. She received an MD from Harvard Medical School, an MBA from Harvard Business School and an MPH from the Harvard T.H. Chan School of Public Health.

Continued research, innovation, and interdisciplinary collaboration are important to unlock the full potential of AI in healthcare. With successful integration, AI is anticipated to revolutionize healthcare, leading to improved patient outcomes, enhanced efficiency, and better access to personalized treatment and quality care. Firstly, comprehensive cybersecurity strategies and robust security measures should be developed and implemented to protect patient data and critical healthcare operations. Collaboration between healthcare organizations, AI researchers, and regulatory bodies is crucial to establishing guidelines and standards for AI algorithms and their use in clinical decision-making.

AI technology identifies changes in blood cells, a potential indicator of a blood disease. In leukemia, for example, algorithms can analyze patients’ medical history, blood cell morphology and genetic data then highlight patterns so subtle that they can be overlooked by human processing. This prompts AI-driven tools to assist medical professionals by “flagging” the presence of potential signs of leukemia, in the early stage. Additionally, AI’s role extends to the analysis of patient records and clinical trial results to establish the effectiveness of new cancer treatments. By employing AI algorithms, researchers can pinpoint specific genetic markers that indicate which patients are most likely to have a positive response to treatment. This stratification could minimize the number of patients who would not benefit from certain treatments, leading to personalized therapies and improving the healthcare outcomes of those needing treatment.

The robotic pet PARO, a baby seal robot, is the most widely used robotic pet and carries various sensors to sense touch, sounds, and visual objects [61]. Another robot is the Mario Kampäi mentioned earlier, which focuses on assisting elderly patients with dementia, loneliness, and isolation. Yet, another companion robot Buddy, by Blue Frog Robotics, assists elderly patients by helping with daily activities such as reminders about medication and appointments, as well as using motion sensors to detect falls and physical inactivity. Altogether, studies investigating cognitive stimulation seem to demonstrate a decrease in the rate of cognitive decline and progression of dementia.

Read more about AI for Way to Revolutionize Medicine here.