Editing Health informatics (section)

== Subject areas ==
[[File:UltrasoundBPH.jpg|250px|thumb|right|An example of an application of informatics in medicine is [[bioimage informatics]].]] Dutch former professor of medical informatics [[Jan van Bemmel]] has described medical informatics as the theoretical and practical aspects of [[Information processing (psychology)|information processing]] and communication based on knowledge and experience derived from processes in medicine and health care.<ref name=":2" />[[File:Use of Fourier transformation chest radiography.jpg|thumb|right|250px|alt=An example of how image processing can be applied to radiography.|An example of how the 2D Fourier transform can be used to remove unwanted information from an [[Projectional radiography|X-ray scan]]]]

The Faculty of Clinical Informatics has identified six high level domains of core competency for clinical informaticians:<ref>{{cite web |title=Core Competency Framework for Clinical Informaticians |url=https://facultyofclinicalinformatics.org.uk/core-competency-framework |website=Faculty of Clinical Informatics |access-date=26 December 2022}}</ref>
* Health and Wellbeing in Practice
* Information Technologies and Systems
* Working with Data and Analytical Methods
* Enabling Human and Organizational Change
* Decision Making
*Leading Informatics Teams and projects.

=== Tools to support practitioners ===
Clinical informaticians use their knowledge of patient care combined with their understanding of informatics concepts, methods, and [[health informatics tools]] to:
* Assess information and knowledge needs of health care professionals, patients and their families.
* Characterize, evaluate, and refine clinical processes,
* Develop, implement, and refine [[clinical decision support system]]s, and
* Lead or participate in the procurement, customization, development, implementation, management, evaluation, and continuous improvement of clinical information systems.
Clinicians collaborate with other health care and information technology [[professionals]] to develop [[health informatics tools]] which promote patient care that is safe, efficient, effective, timely, patient-centered, and equitable. Many clinical informaticists are also computer scientists.

===Telehealth and telemedicine===
{{see also|Telehealth}}

[[File:Операционная. ФЦН (Тюмень) 01.JPG|250px|thumb|right|Telemedicine system. [[Federal Center of Neurosurgery (Tyumen)|Federal Center of Neurosurgery in Tyumen]], 2013]]
[[Telehealth]] is the distribution of health-related services and information via electronic information and telecommunication technologies. It allows long-distance patient and clinician contact, care, advice, reminders, education, intervention, monitoring, and remote admissions. Telemedicine is sometimes used as a synonym, or is used in a more limited sense to describe remote clinical services, such as diagnosis and monitoring. Remote monitoring, also known as self-monitoring or testing, enables medical professionals to monitor a patient remotely using various technological devices. This method is primarily used for managing chronic diseases or specific conditions, such as heart disease, diabetes mellitus, or asthma.

These services can provide comparable health outcomes to traditional in-person patient encounters, supply greater satisfaction to patients, and may be cost-effective.<ref>{{cite journal | vauthors = Salehahmadi Z, Hajialiasghari F | title = Telemedicine in iran: chances and challenges | journal = World Journal of Plastic Surgery | volume = 2 | issue = 1 | pages = 18–25 | date = January 2013 | pmid = 25489500 | pmc = 4238336 }}</ref> Telerehabilitation (or e-rehabilitation[40][41]) is the delivery of rehabilitation services over [[telecommunications network]]s and the Internet. Most types of services fall into two categories: clinical assessment (the patient's functional abilities in his or her environment), and clinical therapy. Some fields of rehabilitation practice that have explored telerehabilitation are: neuropsychology, speech-language pathology, audiology, occupational therapy, and physical therapy. Telerehabilitation can deliver therapy to people who cannot travel to a clinic because the patient has a disability or because of travel time. Telerehabilitation also allows experts in rehabilitation to engage in a clinical consultation at a distance.

===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>

===Clinical Research Informatics===

Clinical research informatics (CRI) is a sub-field of health informatics that tries to improve the efficiency of [[clinical research]] by using informatics methods. Some of the problems tackled by CRI are: creation of [[data warehouse]]s of health care data that can be used for research, support of data collection in [[clinical trials]] by the use of [[electronic data capture]] systems, streamlining ethical approvals and renewals (in [[US]] the responsible entity is the local [[institutional review board]]), maintenance of repositories of past clinical trial data (de-identified). CRI is a fairly new branch of informatics and has met growing pains as any up and coming field does. Some issue CRI faces is the ability for the statisticians and the computer system architects to work with the clinical research staff in designing a system and lack of funding to support the development of a new system.

Researchers and the informatics team have a difficult time coordinating plans and ideas in order to design a system that is easy to use for the research team yet fits in the system requirements of the computer team. The lack of funding can be a hindrance to the development of the CRI.  Many organizations who are performing research are struggling to get financial support to conduct the research, much less invest that money in an informatics system that will not provide them any more income or improve the outcome of the research (Embi, 2009). Ability to integrate data from multiple [[clinical trials]] is an important part of clinical research informatics. Initiatives, such as [[PhenX Toolkit|PhenX]] and [[Patient-Reported Outcomes Measurement Information System]] triggered a general effort to improve secondary use of data collected in past human clinical trials. CDE initiatives, for example, try to allow clinical trial designers to adopt standardized research instruments ([[case report form|electronic case report forms]]).<ref name="pm">{{cite journal | vauthors = Huser V, Shmueli-Blumberg D | title = Data sharing platforms for de-identified data from human clinical trials | journal = Clinical Trials | volume = 15 | issue = 4 | pages = 413–423 | date = August 2018 | pmid = 29676586 | doi = 10.1177/1740774518769655 | s2cid = 4993178 }}</ref>

A parallel effort to standardizing how data is collected are initiatives that offer de-identified patient level clinical study data to be downloaded by researchers who wish to re-use this data. Examples of such platforms are Project Data Sphere,<ref>{{cite web | url=https://www.projectdatasphere.org | title= Share, Integrate & Analyze Cancer Research Data | work = Project Data Sphere }}</ref> [[dbGaP]], ImmPort<ref>{{cite web | url=https://immport.niaid.nih.gov/home |title = ImmPort Private Data | work = National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) | publisher = United States Health and Human Services (HHS) }}</ref> or Clinical Study Data Request.<ref>{{cite web | title = ClinicalStudyDataRequest.com | url = https://ClinicalStudyDataRequest.com | publisher = ideaPoint, Inc }}</ref> Informatics issues in data formats for sharing results (plain [[Comma-separated values|CSV]] files, [[FDA]] endorsed formats, such as [[CDISC]] Study Data Tabulation Model) are important challenges within the field of clinical research informatics. There are a number of activities within clinical research that CRI supports, including:
* More efficient and effective data collection and acquisition
* Improved recruitment into clinical trials
* Optimal protocol design and efficient management
* [[Patient recruitment]] and management
* Adverse event reporting
* Regulatory compliance
* Data storage, transfer,<ref name="odm">{{cite journal | vauthors = Huser V, Sastry C, Breymaier M, Idriss A, Cimino JJ | title = Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) | journal = Journal of Biomedical Informatics | volume = 57 | pages = 88–99 | date = October 2015 | pmid = 26188274 | pmc = 4714951 | doi = 10.1016/j.jbi.2015.06.023 }}</ref> processing and analysis
* Repositories of data from completed clinical trials (for secondary analyses)

[[Image:OMOP (IMEDS) Common Data Model (version 4).png|250px|thumb|right|Example IDR schema]]
One of the fundamental elements of biomedical and translation research is the use of integrated data repositories. A survey conducted in 2010 defined "integrated data repository" (IDR) as a data warehouse incorporating various sources of clinical data to support queries for a range of research-like functions.<ref name=pmid22437072>{{cite journal | vauthors = MacKenzie SL, Wyatt MC, Schuff R, Tenenbaum JD, Anderson N | title = Practices and perspectives on building integrated data repositories: results from a 2010 CTSA survey | journal = Journal of the American Medical Informatics Association | volume = 19 | issue = e1 | pages = e119–24 | date = June 2012 | pmid = 22437072 | pmc = 3392848 | doi = 10.1136/amiajnl-2011-000508 }}</ref> Integrated data repositories are complex systems developed to solve a variety of problems ranging from identity management, protection of confidentiality, semantic and syntactic comparability of data from different sources, and most importantly convenient and flexible query.<ref name=pmid24534444>{{cite journal | vauthors = Wade TD, Zelarney PT, Hum RC, McGee S, Batson DH | title = Using patient lists to add value to integrated data repositories | journal = Journal of Biomedical Informatics | volume = 52 | pages = 72–7 | date = December 2014 | pmid = 24534444 | pmc = 4134416 | doi = 10.1016/j.jbi.2014.02.010 }}</ref>

Development of the field of clinical informatics led to the creation of large data sets with [[electronic health record]] data integrated with other data (such as genomic data). Types of data repositories include operational data stores (ODSs), clinical data warehouses (CDWs), clinical data marts, and clinical registries.<ref name="Nadkarni, P. M 2016. Pp 173-185">{{cite book | vauthors = Nadkarni P |year=2016 |chapter=Clinical Data Repositories: Warehouses, Registries, and the Use of Standards |chapter-url={{Google books|aJbBCQAAQBAJ|page=173|plainurl=yes}} |doi=10.1016/B978-0-12-803130-8.00009-9 |pages=173–85 |title=Clinical Research Computing: A Practitioner's Handbook |publisher=Academic Press |isbn=978-0-12-803145-2 }}</ref> Operational data stores established for extracting, transferring and loading before creating warehouse or data marts.<ref name="Nadkarni, P. M 2016. Pp 173-185" /> Clinical registries repositories have long been in existence, but their contents are disease specific and sometimes considered archaic.<ref name="Nadkarni, P. M 2016. Pp 173-185" /> Clinical data stores and clinical data warehouses are considered fast and reliable. Though these large integrated repositories have impacted clinical research significantly, it still faces challenges and barriers. Following is a list of major patient data warehouses with broad scope (not disease- or [[medical specialty|specialty]]-specific), with variables including laboratory results, pharmacy, age, race, socioeconomic status, comorbidities and longitudinal changes:
{| class="wikitable"
|+ Major patient data warehouses with broad scope 
! Warehouse !! Sponsor !! Main location !! Extent !! Access
|-
| Epic '''Cosmos'''<ref name=Fayanju2025>{{cite journal |vauthors=Fayanju OM, Haut ER, Itani K |title=Practical Guide to Clinical Big Data Sources |journal=JAMA Surg |volume=160 |issue=3 |pages=344–346 |date=March 2025 |pmid=39775674 |doi=10.1001/jamasurg.2024.6006 |url=}}</ref> || [[Epic Systems]] || [[United States]] || 296<ref>{{cite web|title=Epic Cosmos|website=Epic Systems website|url=https://cosmos.epic.com/|accessdate=2025-04-13}}</ref> million patients || Free for participating organizations
|-
| '''PCORnet'''<ref name=Fayanju2025/> || [[Patient-Centered Outcomes Research Institute]] (PCORI) || United States || 140 million patients || Free for participating organizations
|-
| '''OLDW''' (OptumLabs Data Warehouse) || [[Optum]] || United States || 160<ref>{{cite web}url=https://data.ucsf.edu/research/oldw|website=University of California San Francisco|title=OptumLabs Data Warehouse (OLDW)|accessdate=2025-04-13}}</ref> million patients || For a fee, or for free through certain academic institutions<ref>{{cite web|url=https://www.medschool.umaryland.edu/cibr/core/optumlabs/?utm_source=chatgpt.com|title=Optum Labs|website=University of Maryland|accessdate=2025-04-13}}</ref>
|-
| '''EHDEN'''<ref>{{cite journal |vauthors=Voss EA, Blacketer C, van Sandijk S, Moinat M, Kallfelz M, van Speybroeck M, Prieto-Alhambra D, Schuemie MJ, Rijnbeek PR |title=European Health Data & Evidence Network-learnings from building out a standardized international health data network |journal=J Am Med Inform Assoc |volume=31 |issue=1 |pages=209–219 |date=December 2023 |pmid=37952118 |pmc=10746315 |doi=10.1093/jamia/ocad214 |url=}}</ref> (European Health Data Evidence Network) || Innovative Health Initiative of the [[European Union]] || [[Europe]] || 133 million patients || Free for discovery. May have fees for secondary use.<ref>{{cite web}url=https://www.european-health-data-space.com/European_Health_Data_Space_Article_42_%28Proposal_3.5.2022%29.html|title=Articles of the European Health Data Space (EHDS), Article 42, Fees|website=The European Health Data Space (EHDS)|accessdate=2025-04-13}}</ref>
|}

One big problem is the requirement for ethical approval by the institutional review board (IRB) for each research analysis meant for publication.<ref name="Huser, V. 2013 PMC">{{cite journal | vauthors = Huser V, Cimino JJ | title = Don't take your EHR to heaven, donate it to science: legal and research policies for EHR post mortem | journal = Journal of the American Medical Informatics Association | volume = 21 | issue = 1 | pages = 8–12 | year = 2014 | pmid = 23966483 | pmc = 3912713 | doi = 10.1136/amiajnl-2013-002061 }}</ref> Some research resources do not require IRB approval. For example, CDWs with data of deceased patients have been de-identified and IRB approval is not required for their usage.<ref name="Huser, V. 2013 PMC" /><ref name="pmid22437072" /><ref name="Nadkarni, P. M 2016. Pp 173-185" /><ref name="pmid24534444" /> Another challenge is [[data quality]]. Methods that adjust for bias (such as using propensity score matching methods) assume that a complete health record is captured. Tools that examine data quality (e.g., point to missing data) help in discovering data quality problems.<ref>{{cite journal | vauthors = Huser V, DeFalco FJ, Schuemie M, Ryan PB, Shang N, Velez M, Park RW, Boyce RD, Duke J, Khare R, Utidjian L, Bailey C | title = Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets | journal = eGEMs | volume = 4 | issue = 1 | page = 1239 | year = 2016 | pmid = 28154833 | pmc = 5226382 | doi = 10.13063/2327-9214.1239 }}</ref>

===Translational bioinformatics===
Translational Bioinformatics (TBI) is a relatively new field that surfaced in the year of 2000 when human genome sequence was released.<ref name=":0">{{cite journal | vauthors = Tenenbaum JD | title = Translational Bioinformatics: Past, Present, and Future | journal = Genomics, Proteomics & Bioinformatics | volume = 14 | issue = 1 | pages = 31–41 | date = February 2016 | pmid = 26876718 | pmc = 4792852 | doi = 10.1016/j.gpb.2016.01.003 }}</ref> The commonly used definition of TBI is lengthy and could be found on the AMIA website.<ref>AMIA website</ref> In simpler terms, TBI could be defined as a collection of colossal amounts of health related data (biomedical and genomic) and translation of the data into individually tailored clinical entities.<ref name=":0" />
Today, TBI field is categorized into four major themes that are briefly described below:
* Clinical [[big data]] is a collection of electronic health records that are used for innovations. The evidence-based approach that is currently practiced in medicine is suggested to be merged with the practice-based medicine to achieve better outcomes for patients. As CEO of California-based cognitive computing firm Apixio, Darren Schutle, explains that the care can be better fitted to the patient if the data could be collected from various [[medical record]]s, merged, and analyzed. Further, the combination of similar profiles can serve as a basis for personalized medicine pointing to what works and what does not for certain condition (Marr, 2016).
* Genomics in clinical care<br />Genomic data are used to identify the genes involvement in unknown or rare conditions/syndromes. Currently, the most vigorous area of using genomics is oncology. The identification of genomic sequencing of cancer may define reasons of drug(s) sensitivity and resistance during oncological treatment processes.<ref name=":0" />
* Omics for drugs discovery and repurposing<br />Repurposing of the drug is an appealing idea that allows the pharmaceutical companies to sell an already approved drug to treat a different condition/disease that the drug was not initially approved for by the FDA. The observation of "molecular signatures in disease and compare those to signatures observed in cells" points to the possibility of a drug ability to cure and/or relieve symptoms of a disease.<ref name=":0" />
* Personalized genomic testing<br />In the US, several companies offer direct-to-consumer (DTC) [[genetic testing]]. The company that performs the majority of testing is called 23andMe. Utilizing genetic testing in health care raises many ethical, legal and social concerns; one of the main questions is whether the health care providers are ready to include patient-supplied genomic information while providing care that is unbiased (despite the intimate genomic knowledge) and a high quality. The documented examples of incorporating such information into a health care delivery showed both positive and negative impacts on the overall health care related outcomes.<ref name=":0" />

===Medical signal processing===
An important application of [[information engineering]] in medicine is medical signal processing.<ref name=":2" /> It refers to the generation, analysis, and use of signals, which could take many forms such as image, sound, electrical, or biological.<ref>{{cite book| vauthors = Lyons R |title=Understanding Digital Signal Processing|publisher=Prentice Hall|year=2010|isbn=978-0-13-702741-5}}</ref>

===Medical image computing and imaging informatics===
[[File:DiffusionMRI glyphs.png|thumb|right|250px|A mid-axial slice of the ICBM diffusion tensor image template. Each voxel's value is a tensor represented here by an ellipsoid. Color denotes principal orientation: red = left-right, blue=inferior-superior, green = posterior-anterior]]
[[Imaging informatics]] and [[medical image computing]] develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. Those fields aims to extract clinically relevant information or knowledge from medical images and computational analysis of the images. The methods can be grouped into several broad categories: [[image segmentation]], [[image registration]], image-based physiological modeling, and others.

===Medical robotics===
A medical robot is a robot used in the medical sciences. They include surgical robots. These are in most telemanipulators, which use the surgeon's activators on one side to control the "effector" on the other side. There are the following types of medical robots:
* [[Robotic surgery|Surgical robots]]: either allow surgical operations to be carried out with better precision than an unaided human surgeon or allow [[remote surgery]] where a human surgeon is not physically present with the patient.
* [[Rehabilitation robotics|Rehabilitation robots]]: facilitate and support the lives of infirm, elderly people, or those with dysfunction of body parts affecting movement. These robots are also used for rehabilitation and related procedures, such as training and therapy.
* [[Biorobots]]: a group of robots designed to imitate the cognition of humans and animals.
* [[Telerobotics|Telepresence robots]]: allow off-site medical professionals to move, look around, communicate, and participate from remote locations.<ref>{{cite web | vauthors = Corley AM |title=The Reality of Robot Surrogates|url= https://spectrum.ieee.org/robotics/humanoids/the-reality-of-robot-surrogates/0|archive-url= https://web.archive.org/web/20090928075659/http://spectrum.ieee.org/robotics/humanoids/the-reality-of-robot-surrogates/0|url-status= dead|archive-date= September 28, 2009|access-date=19 March 2013|publisher=spectrum.ieee.com|date=September 2009}}</ref>
* [[Pharmacy automation]]: robotic systems to dispense oral solids in a retail pharmacy setting or preparing sterile IV admixtures in a hospital pharmacy setting.
* Companion robot: has the capability to engage emotionally with users keeping them company and alerting if there is a problem with their health.
* Disinfection robot: has the capability to [[disinfect]] a whole room in mere minutes, generally using pulsed [[ultraviolet light]].<ref>{{cite web|url=http://en.topwiki.nl/index.php?title=Pulsed_(UV)_Light&oldid=200|title=Pulsed (UV) Light|publisher=Top Wiki|access-date=2020-12-06|archive-date=2018-09-01|archive-url=https://web.archive.org/web/20180901182639/http://en.topwiki.nl/index.php?title=Pulsed_(UV)_Light&oldid=200|url-status=dead}}</ref><ref>{{cite web|url=http://news.discovery.com/tech/robotics | archive-url = https://web.archive.org/web/20131020074956/http://news.discovery.com/tech/robotics | archive-date = 20 October 2013 |title= On the topic of robotics | work = Discovery News |publisher=Discovery Communications, LLC}}</ref> They are being used to fight [[Ebola virus disease]].<ref>{{cite web|url=http://www.inquisitr.com/1629714/u-s-military-robots-to-join-fight-against-ebola/|title=U.S. Military Robots To Join Fight Against Ebola|publisher=Inquisitr|date=November 23, 2014 | vauthors = Bayot A }}</ref>

===Pathology informatics===
{{Further|Digital pathology}}
[[File:Major topics of pathology informatics.png|thumb|Major topics and processes of pathology informatics: Data management from molecular testing, [[Digital pathology|slide scanning]], [[Digital pathology|digital imaging and image analysis]], networks, databases and [[telepathology]].]]
Pathology informatics is a field that involves the use of information technology, computer systems, and data management to support and enhance the practice of [[pathology]]. It encompasses pathology laboratory operations, data analysis, and the interpretation of pathology-related information.

Key aspects of pathology informatics include:
*[[Laboratory information management system]]s (LIMS): Implementing and managing computer systems specifically designed for pathology departments. These systems help in tracking and managing patient specimens, results, and other pathology data.
*[[Digital pathology]]: Involves the use of digital technology to create, manage, and analyze pathology images. This includes side scanning and automated image analysis.
*[[Telepathology]]: Using technology to enable remote pathology consultation and collaboration. 
*[[Quality assurance]] and reporting: Implementing informatics solutions to ensure the quality and accuracy of pathology processes.