A digitized world, one based on calculations and run by computers no longer remains a concoction of the mind. Artificial Intelligence (AI) systems are rapidly and insidiously bushwhacking their way into our daily reality. This is the tipping point. Our actions now will define the next generation! Think about it. We are uniquely positioned in history to design the evolution of AI. This position also bears the burden of ethically amplifying AI’s potential beneficial contributions with careful selectivity.
AI is a computerized science for the purpose of solving questions and taking over tasks without human input. For this, it makes use of algorithms, heuristics, cognitive computing, and certain rules to derive results. “Siri,” my own virtual assistant, makes use of advanced learning technologies to perform tasks, and answer questions.
Sometimes she’s sassy, but her capabilities are often limited to offering to search the web. One point for humans.
Infused in the medical world, AI machine systems are equipped with large data in various niches of medicine. Using this they become patterned to conduct diagnosis, predict results, and suggest treatments in a manner similar to how a doctor would. However, at this stage, it is the virtual “Electronic Health Records” (EHR) software that has been adopted by almost all modern hospitals. EHR allows for easy data transcription and storage, which ultimately facilitates continuity of patient care between specialists without concerns of illegible handwriting or misplaced paperwork. Dragon Medical One is another time-saving tool for physicians, which transcribes the physician’s spoken words into text to be stored in the patient’s chart. The Dragon software is surprisingly accurate with translating medical data and continuously improving. In Minnesota, this claims to have generated a 167% increase in the quantity of medical documentation that the Allina Health System was able to produce[1], thus, proving AI systems to be more efficient than human power. One point for AI.
AI can be programmed to understand, summarize, and categorize large amounts of data, simplifying work that is otherwise tedious when undertaken by medical personnel. AI can recognize and flag complicated cases for follow up, offering physicians more time to improve their efficiency, as well as more time to spend with patients.[2] Tied point: solid teamwork
Furthermore, AI can recognize patterns in patient cohorts and in medical literature and create new categorization of data. By this definition, approximately 80% of healthcare data is unstructured[2]. AI reads through the data and extracts important clinical information – which supports medical diagnosis, as evidenced by application of IBM Watson AI technologies at Memorial Sloan Kettering Cancer Center. In 2016, AI swiftly rose to the challenge of diagnosing a rare leukemia by cross-referencing 20 million oncology records.
Through its data reading capabilities, AI has also made its entrance in the sphere of genetic testing. GenomeBrain is a platform which recommends suitable genetic tests for the individual by careful study of medical and ethnic factors. It matches the patient history with a suitable genetic test in under 10 minutes[3] – efficient and effective. Point for AI.
Another recent - potentially remarkable - application of AI technology is being demonstrated in the search of a treatment for fibrosis. Fibrosis is a condition that changes connective tissues in the form of a thickened and scarred texture. Fibrosis can affect the skin and internal organs, progressively interrupting lung and liver function. Physicians may be a step closer to the treatment of fibrosis thanks to the invention of Generative Tensorial Reinforcement Learning (GENTRL) by Insilico[4]. This machine learning program basically generates 30,000 molecules with the purpose of combat against Fibrosis. In a matter of 25 days, 6 such compounds were produced, which led to display favorable results in mice. This meant the ability of conducting a pre-clinical test in merely 50 days. Such successful programing led to a partnership between Insilico Medicine and Jiangsu Chia Tai Fenghai Pharmaceutical of up to $200 million[5]. As a drug discovery partnership, it aims to carry out research and development, and extend its ability of generating viable compounds, to other areas of incurable diseases. Point TBD.
Jumping on the bandwagon: with $17 billion of investments in medical AI since 2009 and almost $37 billion worth of investments anticipated by 2025, it’s clear that some of the world’s strongest players are in the game, trying to crack the code of next-level AI applications. CVS Health partnered with IBM to apply AI in chronic disease treatment, while Johnson & Johnson is using AI to sift through years of scientific research and extract new ideas for drug development.2 The future prospects of AI hint at refined radiology tools, alongside immediate assessment and analysis[6] of medical tests. Implications of AI analyzing genetic data, could include physicians warning patients of disease prior to the onset of visible symptoms, or perhaps developing treatments customized to suit the genetic structure of each individual. Point TBD.
However, the notion of introducing AI to the field of medicine brings with itself some doubts. There is the thought that these machines will eventually replace human doctors. Alongside this evolution run concerns that AI do not have the full spectrum of instinct, experience and feeling as a human physician in making a diagnosis, thus the machine analysis could be faulty. As machines rely on data, and the correct working of the software, there are technical hazards and irregularities accompanying each function too. The dilemma of ethical allowance also poses itself as the data is stored and spread on a database, making it accessible to everyone who gets access, with or without consent. Worse, there is concern that data can be sold to third-party users[7] for purposes unknown – possibly resulting in criminal claims. I’ll be watching Law & Order.
AI in the medical world has brought its set of advantages, and continues to grow, one research and development project at a time, but it also poses concerns, like any other technology. Although, it may be far-fetched in the immediate future to think AI is developed to replace human jobs, it is a reasonable consideration to look out for such a trend over the next few years. At the moment, AI assists the work of medical professionals, enhancing ease of work. With increasing investments and joint ventures, it is no surprise that merging AI and medicine has become a growing trend underlying new project developments. Next point in the game goes to….?
#Innervate #Innervation #InnervateWorldwide #Medicine2025 #HeySiri #VirtualAssistant #CVSHealth #CVS #IBMWatson #JNJ #JohnsonAndJohnson #Insilico #InsilicoMedicine #Oncology #Leukemia #Fibrosis #ChronicDisease #MedicalSoftware #EMR #ElectronicMedicalRecords #HealthcareInvestments #GamePoint #Medicine #Doctor #Physician #Scientist #DragonMedicalOne #DictationSoftware #Diagnosis #Analysis #PatientCare #Clinical #Preclinical #ClinicalTrials #MedicalResearch #Science #NextLevel #MedicalTechnology #Neurology #ElectricalImpulse #Technology #MedicalSpeechRecognition #Biotechnology #Pharmacology #GENTRL #GenerativeTensorialReinforcementLearning #GenomeBrain #GenomeSmart #JiangsuChiaTaiFenghaiPharmaceutical #Breakthrough #Project #Development #Trending #HealthcareTrends #MedicalTrends #GeneticTestic #Genome #Radiology #ArtificialIntelligence #AI #MedicalAI #Ethics #EthicalDilemma #JointVentures #Genetics #Drug #DrugDiscovery #Pipeline #InThePipeline #CrackTheCode
References:
1. Mejia N. AI for Electronic Health Records (EHRs) and Electronic Medical Records (EMRs). Emerj. https://emerj.com/ai-sector-overviews/ai-for-electronic-health-records-ehrs-and-electronic-medical-records-emrs/. Published December 17, 2018. Accessed October 30, 2019.
2. Artificial Intelligence in Medicine | Machine Learning. Ibm.com. https://www.ibm.com/watson-health/learn/artificial-intelligence-medicine. Published 2019. Accessed October 28, 2019.
3. 3. GenomeSmart™ Announces the Launch of GenomeBrain™, a Comprehensive Genetic Test Recommendation Platform, Designed to Simplify and Support the Expanded Use of Genetic Testing in Healthcare | BioSpace. BioSpace. https://www.biospace.com/article/releases/genomesmart-announces-the-launch-of-genomebrain-a-comprehensive-genetic-test-recommendation-platform-designed-to-simplify-and-support-the-expanded-use-of-genetic-testing-in-healthcare/. Published 2019. Accessed October 28, 2019.
4. 4. Hale C. Medtech Insilico's AI Networks Generate Custom Lead Compounds For Fibrosis In Less Than 50 Days.; 2019. https://www.fiercebiotech.com/medtech/insilico-s-ai-networks-generate-custom-lead-compounds-for-fibrosis-less-than-50-days. Accessed October 28, 2019.
5. 5. Hale C. Insilico Signs $200M AI Drug Discovery Partnership With China's CTFH.; 2019. https://www.fiercebiotech.com/medtech/insilico-signs-200m-ai-drug-discovery-partnership-china-s-ctfh. Accessed October 28, 2019.
6. Buch VH, Ahmed I, Maruthappu M. Artificial intelligence in medicine: current trends and future possibilities. Br J Gen Pract. 2018;68(668):143–144.
7. Artificial intelligence in medicine raises legal and ethical concerns. The Conversation. https://theconversation.com/artificial-intelligence-in-medicine-raises-legal-and-ethical-concerns-122504. Published 2019. Accessed October 28, 2019.
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