Editing Health informatics (section)

===Decision support, artificial intelligence and machine learning in healthcare===
{{see also|Artificial intelligence in healthcare}}

[[File:X-ray of hand, where bone age is automatically found by BoneXpert software.jpg|thumb|250px|right|[[Projectional radiography|X-ray]] of a hand, with automatic calculation of [[bone age]] by a computer software]]
A pioneer in the use of [[artificial intelligence in healthcare]] was American biomedical informatician [[Edward H. Shortliffe]]. This field deals with utilization of machine-learning algorithms and artificial intelligence, to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data. AI programs are applied to practices such as diagnosis processes, [[Protocol system|treatment protocol development]], [[drug development]], personalized medicine, and patient monitoring and care. A large part of industry focus of implementation of AI in the healthcare sector is in the [[clinical decision support system]]s.

As more data is collected, machine learning algorithms adapt and allow for more robust responses and solutions.<ref name=":03">{{cite journal | vauthors = Pisarchik AN, Maksimenko VA, Hramov AE | title = From Novel Technology to Novel Applications: Comment on "An Integrated Brain-Machine Interface Platform With Thousands of Channels" by Elon Musk and Neuralink | journal = Journal of Medical Internet Research | volume = 21 | issue = 10 | pages = e16356 | date = October 2019 | pmid = 31674923 | doi = 10.2196/16356 | pmc = 6914250 | url = https://www.jmir.org/2019/10/e16356/ | s2cid = 207818415 | doi-access = free }}</ref> Numerous companies are exploring the possibilities of the incorporation of [[big data]] in the healthcare industry. Many companies investigate the market opportunities through the realms of "data assessment, storage, management, and analysis technologies" which are all crucial parts of the healthcare industry.<ref name=":14">{{cite journal|last1=Quan|first1=Xiaohong Iris|last2=Sanderson|first2=Jihong | name-list-style = vanc |date=December 2018|title=Understanding the Artificial Intelligence Business Ecosystem|url=https://ieeexplore.ieee.org/document/8540793|journal=IEEE Engineering Management Review|volume=46|issue=4|pages=22–25|doi=10.1109/EMR.2018.2882430|s2cid=59525052|issn=0360-8581|url-access=subscription}}</ref> The following are examples of large companies that have contributed to AI algorithms for use in healthcare:

* IBM's [[IBM Watson|Watson]] Oncology is in development at [[Memorial Sloan Kettering Cancer Center]] and [[Cleveland Clinic]]. IBM is also working with [[CVS Health]] on AI applications in chronic disease treatment and with [[Johnson & Johnson]] on analysis of scientific papers to find new connections for [[drug development]]. In May 2017, IBM and [[Rensselaer Polytechnic Institute]] began a joint project entitled Health Empowerment by Analytics, Learning and Semantics (HEALS), to explore using AI technology to enhance healthcare.
* [[Microsoft]]'s Hanover project, in partnership with [[Oregon Health & Science University]]'s Knight Cancer Institute, analyzes medical research to predict the most effective [[cancer]] drug treatment options for patients. Other projects include medical image analysis of tumor progression and the development of programmable cells.<ref>{{Cite web |title=Microsoft's next big AI project? Helping 'solve' cancer |url=https://www.zdnet.com/article/microsofts-next-big-ai-project-helping-solve-cancer/ |access-date=2024-09-29 |website=ZDNET |language=en}}</ref>
* [[Google]]'s [[DeepMind]] platform is being used by the UK [[National Health Service]] to detect certain health risks through data collected via a mobile app. A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
* [[Tencent]] is working on several medical systems and services. These include AI Medical Innovation System (AIMIS), an AI-powered diagnostic medical imaging service; WeChat Intelligent Healthcare; and Tencent Doctorwork.
* Intel's venture capital arm [[Intel Capital]] recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.
* Kheiron Medical developed deep learning software to detect [[breast cancer]]s in [[mammogram]]s.
* [[Fractal Analytics]] has incubated Qure.ai which focuses on using deep learning and AI to improve radiology and speed up the analysis of diagnostic x-rays.
* [[File:Elon Musk and the Neuralink Future.jpg|thumb|250px|right|Elon Musk premiering the surgical robot that implants the Neuralink brain chip]] [[Neuralink]] has come up with a next generation [[neuroprosthetic]] which intricately interfaces with thousands of neural pathways in the brain.<ref name=":03" /> Their process allows a chip, roughly the size of a quarter, to be inserted in place of a chunk of skull by a precision surgical robot to avoid accidental injury.<ref name=":03" />

Digital consultant apps like [[Babylon Health|Babylon Health's GP at Hand]], [[Ada Health]], [[Alibaba Health]] [[Doctor You]], KareXpert and [[Your.MD]] use AI to give medical consultation based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to compare against a database of illnesses. Babylon then offers a recommended action, taking into account the user's medical history. Entrepreneurs in healthcare have been effectively using seven business model archetypes to take AI solution[<nowiki/>[[wikipedia:Use plain English#Buzzwords|buzzword]]] to the marketplace. These archetypes depend on the value generated for the target user (e.g. patient focus vs. healthcare provider and payer focus) and value capturing mechanisms (e.g. providing information or connecting stakeholders). [[IFlytek]] launched a service robot "Xiao Man", which integrated artificial intelligence technology to identify the registered customer and provide personalized recommendations in medical areas.

It also works in the field of medical imaging. Similar robots are also being made by companies such as UBTECH ("Cruzr") and [[Softbank]] Robotics ("Pepper"). The Indian startup [[Haptik]] recently developed a [[WhatsApp]] chatbot which answers questions associated with the deadly [[coronavirus]] in [[India]]. With the market for AI expanding constantly, large tech companies such as Apple, Google, Amazon, and Baidu all have their own AI research divisions, as well as millions of dollars allocated for acquisition of smaller AI based companies.<ref name=":14" /> Many automobile manufacturers are beginning to use machine learning healthcare in their cars as well.<ref name=":14" /> Companies such as [[BMW]], [[GE]], [[Tesla, Inc.|Tesla]], [[Toyota]], and [[Volvo]] all have new research campaigns to find ways of learning a driver's vital statistics to ensure they are awake, paying attention to the road, and not under the influence of substances or in emotional distress.<ref name=":14" /> Examples of projects in computational health informatics include the COACH project.<ref>{{cite journal | first1 = Jesse | last1 = Hoey | first2 = Pascal | last2 = Poupart | first3 = Axel | last3 = von Bertoldi | first4 = Tammy | last4 = Craig | first5 = Craig | last5 = Boutilier | first6 = Alex | last6 = Mihailidis | name-list-style = vanc |title= Automated Handwashing Assistance For Persons With Dementia Using Video and a Partially Observable Markov Decision Process|journal=Computer Vision and Image Understanding |volume=114 |issue=5 |pages= 503–19|year=2010 |doi=10.1016/j.cviu.2009.06.008| citeseerx = 10.1.1.160.8351 | s2cid = 8255735 }}</ref><ref name="Mihailidis+2008">{{cite journal | vauthors = Mihailidis A, Boger JN, Craig T, Hoey J | title = The COACH prompting system to assist older adults with dementia through handwashing: an efficacy study | journal = BMC Geriatrics | volume = 8 | page = 28 | date = November 2008 | pmid = 18992135 | pmc = 2588599 | doi = 10.1186/1471-2318-8-28 | doi-access = free }}</ref>