Tuesday, February 3, 2026

Combinatorics Summary....

 



 

I realized that I have a combinatorics thread running through my blog across several subjects.  I have been interested in combinatorics since I sent an email to Robert Spitzer on the various combinations of diagnostic criteria.  His only comment was “Interesting”.  Since then, I have commented on a post that purported to discredit psychiatric diagnoses based on combination of diagnostic criteria (too many), a study of the real combinations of major depression diagnoses, and character and word phrase combinations for encryption and password protection.  I went as far as getting dice and using them to construct passphrases of varying length using the Electronic Frontier Foundation (EFF) word list for that purpose.

If you have no experience with combinations or it has been a long time since your college statistics course – dice are a good place to start.  Each die has 6 sides with corresponding numbers. The total combinations possible are 6n, where n = the number of dice rolled at once.  The EFF world list is 6,667 word long and that happens to be 66.  So, to generate passphrases – 5 dice are rolled and the corresponding number is looked up on the word list and recorded.  The process is repeated until the desired phrase length is generated.  The only downside to this method is that some sites still insist on additional numbers and special characters.  They can still be inserted in the passphrase, but other systems like hexadecimal may be more convenient.  The advantage to passphrases is that they are theoretically easier to memorize and type without error.  That breaks down with very long phrases.

In biology and medicine, combinatorics can be applied at several levels. Some have more meaning than others.  On this blog, I responded to a paper suggesting that the possible combinations of diagnostic criteria meant that psychiatric diagnoses were meaningless and unscientific.  The lesson from this post is to have an idea of what you are counting and what it means. The total combinations of verbal criteria depend a lot on the phrasing and the total number of criteria whether large or small is not necessarily disqualifying as illustrated in this post.  The combinatorial upper limit can be unrealistically large based on how it is defined and just running the numbers does not mean that all possible combinations will be found.  There also seems to be some magical thinking involved – just because you count something does not say anything about what that means.  It is quite literally an exercise in the map is not the territory. 

I looked at a second paper where the authors looked at a lower number of combinations based on the DSM diagnostic criteria for major depression.  In that case the total number of diagnoses was much lower at 227 combinations.  The authors of that second paper did standardized interviews on 3,800 people and of the 1,566 with major depression – just 10 of those combinations accounted for 50% of the cases.  About ¼ of the possible combinations (57/227) did not occur in any group.  This paper is a stark reminder that just counting things in biology or medicine doesn’t necessarily mean anything.

That brings me to the concept of how we make sense out of the most valid combinatorial explosions in medicine. For me validity is baked into the biology and not a verbal description of things.  The backing for that comes from biological taxonomy and the fact that molecular biology and genomics is solving problems that could not be solved by the verbal description of direct observations in the Linnean tradition.  To that end I am reproducing a table below that is all about the polygenic risk for bipolar disorder. 



Note that in this table the authors are estimating the total possible combinations of 803 polygenes. The theoretical number of possible combinations can be calculated using the formula n! / r!((nr)!,where n represents the number of genetic variants analyzed in a study, and r represents the number of genetic variants per combination. In the case of  SNP genotypes,3^r.the formula is n! / r!(n-r)! ×3^r.  The authors point out that the lowest value for r is 2 but the upper limit is unknown.  They also show how the number of combinations can be limited experimentally.  Of the 57,911,211 combinations found only in patients and not controls they could all be random but there were a significant number of SNPs associated with different groupings in bipolar disorder.    

Using the equations from above in a more readable graphic form:

 

 

Substitution yields the following:

- from the top equation, for 100 variants the theoretical 10-variant combinations would be 1.73 x 1013

- from the bottom equation, for 500,000 SNPs analyzed there would be 2.3 × 1012 two-variant combinations and 3.4 × 1018 three variant combinations.

The application of practical measure includes scanning SNPs for varying combination lengths in the population of interest relative to controls. At lower numbers those combinations can be taken out scanning for longer combinations. A further simplification is to scan only for combinations found in patient populations.  An example of that study is included in the tables below for 803 SNPs in 607 bipolar disorder patients and  1,354 controls. 

Cluster and subgroup analysis is required in very heterogeneous conditions to analyze clusters containing a specific SNP, the distribution of SNP genotypes relative to controls, and cluster selection that contains an SNP for a specific biological function.  Using this kind of analysis 73/609 bipolar disorder patients had these clusters compared to none in the control population. 

While the SNP and variant analysis in 2017 is a good example of combinatoric applications – it did not address the problem of missing heritability.  Missing heritability is the difference between what is observed in familial heritability studies and what is predicted with genetic analysis.  Looking at the predictions from SNP based analysis only a low percentage of familial inheritance was predicted.  That improved with more sensitive analytical techniques that considered additional genetic mechanisms.  The additional mechanisms included SNV (single nucleotide variation), insertions or deletions (indels), SVs (structural variations), CNV (copy number variations), and STR (short tandem repeat (3-5).  Applications that identify all these variations are much more likely to predict the heritability of the pedigree than earlier techniques.  I hope to revisit some of these genetic innovations in an upcoming post about the DSM-6 proposals.

 

George Dawson, MD, DFAPA 

 

References:

1:  Mellerup E, Møller GL. Combinations of Genetic Variants Occurring Exclusively in Patients. Comput Struct Biotechnol J. 2017 Mar 10;15:286-289. doi: 10.1016/j.csbj.2017.03.001. PMID: 28377798; PMCID: PMC5367802.

2:  Koefoed P, Andreassen OA, Bennike B, Dam H, Djurovic S, Hansen T, Jorgensen MB, Kessing LV, Melle I, Møller GL, Mors O, Werge T, Mellerup E. Combinations of SNPs related to signal transduction in bipolar disorder. PLoS One. 2011;6(8):e23812. doi: 10.1371/journal.pone.0023812. Epub 2011 Aug 29. PMID: 21897858; PMCID: PMC3163586.

3:  Behera S, Catreux S, Rossi M, Truong S, Huang Z, Ruehle M, Visvanath A, Parnaby G, Roddey C, Onuchic V, Finocchio A, Cameron DL, English A, Mehtalia S, Han J, Mehio R, Sedlazeck FJ. Comprehensive genome analysis and variant detection at scale using DRAGEN. Nat Biotechnol. 2025 Jul;43(7):1177-1191. doi: 10.1038/s41587-024-02382-1. Epub 2024 Oct 25. PMID: 39455800; PMCID: PMC12022141.

4:  Wainschtein P, Zhang Y, Schwartzentruber J, Kassam I, Sidorenko J, Fiziev PP, Wang H, McRae J, Border R, Zaitlen N, Sankararaman S, Goddard ME, Zeng J, Visscher PM, Farh KK, Yengo L. Estimation and mapping of the missing heritability of human phenotypes. Nature. 2026 Jan;649(8099):1219-1227. doi: 10.1038/s41586-025-09720-6. Epub 2025 Nov 12. PMID: 41225014; PMCID: PMC12851931.

5:  Grotzinger AD, Werme J, Peyrot WJ, Frei O, de Leeuw C, Bicks LK, Guo Q, Margolis MP, Coombes BJ, Batzler A, Pazdernik V, Biernacka JM, Andreassen OA, Anttila V, Børglum AD, Breen G, Cai N, Demontis D, Edenberg HJ, Faraone SV, Franke B, Gandal MJ, Gelernter J, Hatoum AS, Hettema JM, Johnson EC, Jonas KG, Knowles JA, Koenen KC, Maihofer AX, Mallard TT, Mattheisen M, Mitchell KS, Neale BM, Nievergelt CM, Nurnberger JI, O'Connell KS, Peterson RE, Robinson EB, Sanchez-Roige SS, Santangelo SL, Scharf JM, Stefansson H, Stefansson K, Stein MB, Strom NI, Thornton LM, Tucker-Drob EM, Verhulst B, Waldman ID, Walters GB, Wray NR, Yu D; Anxiety Disorders Working Group of the Psychiatric Genomics Consortium; Attention-Deficit/Hyperactivity Disorder (ADHD) Working Group of the Psychiatric Genomics Consortium; Autism Spectrum Disorders Working Group of the Psychiatric Genomics Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium; Eating Disorders Working Group of the Psychiatric Genomics Consortium; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; Nicotine Dependence GenOmics (iNDiGO) Consortium; Obsessive-Compulsive Disorder and Tourette Syndrome Working Group of the Psychiatric Genomics Consortium; Post-Traumatic Stress Disorder Working Group of the Psychiatric Genomics Consortium; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Substance Use Disorders Working Group of the Psychiatric Genomics Consortium; Lee PH, Kendler KS, Smoller JW. Mapping the genetic landscape across 14 psychiatric disorders. Nature. 2026 Jan;649(8096):406-415. doi: 10.1038/s41586-025-09820-3. Epub 2025 Dec 10. PMID: 41372416; PMCID: PMC12779569.

 

Graphics Credit:

Table 1 is reused from open access reference 1 above per  CC BY license (http://creativecommons.org/licenses/by/4.0/). 

 

Wednesday, January 28, 2026

The 12 Word Note...

 


I had one of my typical work-related nightmares last night.  Three years into retirement and I am still anxious about work. I think it is an interesting exercise in how human consciousness works and how it led to the above graphic so here goes.

As usual it starts out in a medical center, in this case what appears to me a medical unit in a large hospital.  I have never been in this specific setting before, but it has a similar arrangement to many units where I have worked.  There is a walled off central square nurses’ station surrounded by a ten-foot-wide walking area leading to the patient rooms.  Judging by the equipment and floor coverings it is from about 1980 to 2000.  I am seated in the nurses’ station and looking at a chart.  I have been consulted to see someone on that floor and review the chart.  There are no computers, everything is written on paper, and the chart clips it all together at the top.  As a result, the notes must be flipped on the short edge to maintain the same reading orientation. The setting is drab but in color.  There is a lot of activity but nothing overly hectic that would indicate an emergency.

I look at a note from the pervious consultant.  It is in outline form with headings only and spaces to be filled in later.  The chart notes are all lined in a typical tablet format.  There is about a page and a half and then there is about a ½ page narrative written like a paragraph by a different staff person.  I stare at the space between the two notes.  That space is about 1 ½ pages in length.  I start thinking about how I can squeeze my typically micrographic consult note into 1 ½ pages. 

I start to get anxious.  I realize I am just staring at the chart and not doing anything.  I look at the doorway to the room containing the patient I am supposed to see.  I feel like I am frozen and I will never get out of there if I don’t get in there and figure out how to record my note.  I start to panic.  I wake up anxious with an elevated heart rate.

After laying in bed for a while and thinking about the dream, I associate to the Milwaukee VA and my first Internal Medicine rotation. The charts in the dream seem like old VA charts.  I remember writing in those charts – very long histories and physicals and consult notes and progress notes that were considerably shorter.  I remembered a neurosurgery rotation where a handful of us covered an entire floor of very ill patients, a neurosurgery ICU, and all the emergency room and general consults from two large hospitals.  The only possible way we could do that was by completing all the rounding and documentation in 2-3 hours because we were also expected to be in the OR for many hours per day.  The typical rounding note was less than 20 words – “Afebrile; VSS; (brief description of neuro status and surgical wound).” 

That led me to think about the shortest Internal Medicine note by a resident.  Internal Medicine residents were some of the smartest and most industrious people I have ever met.  But in the 1980s they (and me their intern) were seriously abused.  A typical call schedule was on call every third night.  On a call night 10 admissions all night long of very ill patients while cross covering the entire hospital was typical. That usually meant no sleep – but you were expected to do the usual work until 5PM on your sleep deprived day until you could go home and sleep. This resulted in some embarrassing moments like falling asleep while writing notes or in one case – not putting the note in the chart until my resident reminded me, I was still carrying it around on my clipboard.     

One day in that VA hospital a friend of mine said “I want show you something”.  He showed me a note written by his resident that was written in 48-point font.  The only reason I know that is because of the experiment at the top of this page (that is my standard 12 point at the top and 48 point at the bottom).  It was the largest cursive I had ever seen and there were only 3 or 4 words per line.  We both laughed about it.  My friend told me all his notes were like that, and they were signed off by attendings. He was basically writing down 12-15 words per page to my 300-400.  We did not take the time to analyze the content of the notes – we were just amazed by the form. 

This morning, I thought about whether it was possible to write all your notes like that – even in 1982.  It certainly would not pass muster in the modern era of excessive and often useless documentation required by governments and insurance companies. He would get audited for “bullet points” and would never get reimbursed.  In the modern world every note needs to contain bullet points of specific documentation whether it is relevant to the medical care or not.   I was in a mandatory seminar in the late 1990s when an FBI agent claimed that any physician not completing the bullet points could be charged with mail fraud and sentenced to federal prison on a RICO violation. That was before 911 and the FBI discovered they could probably be doing better things than threatening physicians.

At times, I get the mental image of hard drives spinning in large storage arrays containing all this worthless medical documentation that will never be read again.  I used to think it was a high environmental burden until large language model AI came on the scene.  Now these AI companies are building their own power plants just so the AI can scan vast amounts of worthwhile and worthless documents to synthesize an answer that may contain some accurate data or it may be an AI hallucination/confabulation - like a medical reference that does not exist.

All those thoughts sprang from a bad dream of an old, retired guy who at times wishes he was still working.  But at other times realizes that retirement is a good idea.

 

George Dawson, MD, DFAPA

 

Supplementary:

Maybe Hospital Dreams could be a sequel to Train Dreams.  I tried to work it in, but it did not fit. 

 

References:

1.  Arvikar SL, Schaefer PW, Lemieux JE, Steere AC. Case 3-2026: A 58-Year-Old Woman with Diplopia and Fever. N Engl J Med. 2026 Jan 22;394(4):383-391. doi: 10.1056/NEJMcpc2412529. PMID: 41564400.

My test sentence above is the first line in this week’s Case records of the MGH.  One sentence is fair use. 

Tuesday, January 27, 2026

Dermatology Informs the Rhetoric About Psychiatry

 


I have posted in the past about the similarities between rheumatology and psychiatry.  The classification systems are the same, there is a lot of diagnostic flexibility, all of the conditions are very heterogenous, the underlying pathophysiology is not clear, and the mechanisms of action of most of the treatments used are unknown.  I thought I would do a similar comparison with Dermatology.  As an acute care psychiatrist I noticed that dermatology problems are frequently ignored by both patients and physicians or treated incorrectly with over-the-counter preparations.  There is some overlap with both psychiatric and neurological conditions, but most of the skin conditions I detected were not in that category.  I was always grateful I had specialists available who could see my patients quickly.  In some cases, the treatment was lifesaving.

Dermatology is a classic example of pattern-matching in diagnoses and if you missed it I will post my favorite case here.  It happened in medical school on an infectious disease rotation. We were asked to see a patient for spontaneous bacterial peritonitis, an infection of ascitic fluid in the abdomen.  After reviewing all the preliminaries, we came into the patient’s room with the attending.  From across the room the attending said: “What am I seeing from here that needs to be addressed?”   We all looked puzzled.  He came across the room and pointed out a large confluent pink rash on the man’s left ankle. He aspirated a small sample from the edge of the rash and sent it to his lab for further analysis. He was an expert in streptococcal infections and guessed what type of strep it would be.  He picked an antibiotic that he thought would work for both conditions.    

The prevalence and comorbidly of dermatology diseases is high.  At any given moment 25-33% of the world’s population has one of these diseases (1).  That may be as high as one in three Americans in the US (2).  Some studies have suggested the point prevalence may be much higher (up to 64% in some studies) because people are unaware of the fact that they have the diseases (like rosacea and actinic keratoses). Studies also have variable inclusion criteria for the 5 most common diseases to all possible diseases.  Variability also exists within the same category like atopic dermatitis that can range from  2.6 – 9.6% and in some cases those authors point to variable diagnostic criteria.  To illustrate some of this variability consider the following case:

66 YO man with a history of asthma and anaphylactic reactions.  No history of atopic dermatitis as a child but newly diagnosed in his 60s when it presented with intense pruritic and patchy crusty lesions that were not associated with scratching. He also had a recent 24 hr. cardiac monitor and had similar crusting lesions at the electrode sites for two weeks after they were removed. He has also been seeing a dry eye specialist for a severe dry eye problem that interferes with his work.  The dry eye specialist has diagnosed Meibomian Gland Dysfunction. He uses artificial tears 6-8 times a day, eyelid scrubs, and occasional ocular glucocorticoids for relief.  On exam he is noted to have patchy lesions on his shoulders, an erythematous rash on his right medial thigh, and an erythematous rash with skin peeling on his right palm.  Examination of the scalp shows flaky dandruff with oily inflamed patches.  He has areas of facial induration with some facial acne with redness and indurated subcutaneous patches not associated with the acne. Some of of those areas of induration are tender. 

The final dermatology diagnoses based on exam and clinical picture are:  atopic dermatitis, contact dermatitis, rosacea, seborrheic dermatitis of the scalp, and probable ocular rosacea. The recommended treatment includes a topical facial medication containing azelaic acid, metronidazole, and ivermectin, topical glucocorticoids for the seborrhea and atopic dermatitis, prescription strength (5%) ketoconazole shampoo twice a week, and CeraVe applied to all areas of the body with active rash or pruritic from atopic dermatitis. None of the medications are curative and are to be used on a maintenance basis as needed with annual follow up visits.  Dry eye follow up is separate per that specialist.  Despite 3 diagnoses, this patient has 5 distinct lesions for atopic dermatitis, 4 distinct lesions for rosacea, and 2 for seborrhea.

How does all this aid in understanding the typical social media criticisms of psychiatry?  Here are a few:

1:  Number of diagnoses:  I have looked at this issue in detail and counted the diagnoses in the DSM-5 several ways. According to the American Board of Dermatology, dermatologists are trained to recognize and treat over 3,000 diagnoses of skin disease. Does it seem reasonable that the most complex organ in the human body might have at least 281?

2:  High prevalence:  typical social media criticisms of psychiatry focus on the high prevalence of disorders and the treatment of those disorders.  Many wellness industry influencers use this as a basis for suggest that lifestyle changes and whatever products they are hawking are the real solution and the problem is excessive medical care. Some point prevalence studies suggest that dermatology conditions may be as high as 68% of the population and that there are always millions with the most common conditions like atopic dermatitis (2-10%), seborrheic dermatitis (4%), and psoriasis (2-3%).  There are also single syndromes that have very high reported prevalence in the literature like Sensitive Skin Syndrome that is reported to have a prevalence of 60-70% in women and 50-60% in men.  Self-reported skin sensitivity decreased with age and reported severity.  11% of men and 19% of women 25 years of age or less reported their skin was sensitive or very sensitive compared to 7% and 12% respectively at or greater than age 55. (20)

3:  Heterogeneity: Social media criticism and some research go to absurd lengths to show that psychiatric conditions are heterogeneous.  When that is studied in dermatology the heterogeneity is just as significant. The reality is that biology produces heterogeneous individuals and diseases and heterogeneous diseases in the same individual.   

As an illustration, consider the following simple calculation the flows from polygenes or a disorder determined by multiple genes.  A recent study of major depression (23) found 697 associations across 636 loci.  Since each locus has the 3 possible states (homozygous dominant - AA, heterozygous - Aa, and homozygous recessive - aa) a first approximation of the total number of gene combinations is 3^636 = 2.812 x 10^303.  This is an impossibly large number, but it does indicate the massive amount of information that relatively simple biological configurations can carry.  On this blog I have examined attempts to look at the combinations of diagnostic criteria and their real-life shortcomings.  The best contrast was a group who suggested that large number of diagnostic feature combinations meant that psychiatric diagnoses were “unscientific” and a group who actually examined the features in clinical practice and determined they were manageable.  The latter group showed that there were 225 possible combinations for major depression and about ¼ of them did not appear in a single patient.

The above approximation based on polygenes is problematic at a couple of levels – foremost is that not all polygenes are as likely to be associated with the disorder.  That analysis requires more advanced statistical methods (24).  But even considering modest number of contributing genes explains heterogeneity, severity differences, and treatment resistance.  I will take it a step further and suggest that anyone who does not appreciate that heterogeneity is a feature of biology and not a problem for diagnosis lacks a basic understanding of biology and medicine.  

4: Comorbidity:  A 2022 study showed that (after excluding cancer screening) 43.35% reported having had at least one dermatological condition or disease in the past 12 months with 35.38% had one skin disease, 24.32% had two skin diseases, 14.06% had three skin diseases, and 26.34% had four skin diseases or more.  Critics seem to think that comorbidity is a weakness of psychiatric diagnosis when every other specialty recognizes equivalent or greater amounts of comorbidity.

5:  Diagnostic certainty:  In the past I taught a course in diagnostic thinking to medical students.  One of my cited references was a paper comparing the diagnostic expertise of dermatologists to primary care physicians on a standard set of photos about dermatology diagnoses (13).  The evidence on evaluating clear cut cases of dermatological disease and equivocal cases the dermatologists are much better than non-specialist physicians.  The diagnostic process in dermatology also suggests that pattern-matching is probably a more significant factor than rules-based processes for experienced physicians. By rules based processes – I mean written diagnostic criteria. There is no reason to think that pattern matching does not apply in psychiatry at several levels.

Pattern matching also speaks to different phenotypes in the same individual. In the case example, this man has several different equivalents of the same underlying disease – 5 variants of atopic dermatitis and 2 for rosacea all on his body at the same time.    

6:  Underlying pathophysiology:  The standard social media caricature of psychiatry is that it is a poorly defined morass of conditions with no known specific etiologies or pathophysiology.  In fact, 67% of DSM listed diagnoses have either a known pathophysiology or a specific medical test.  Every psychiatric diagnosis has a medical differential diagnosis.  It is why psychiatrists are medical specialists. The same is true for dermatological disorders and there are many cutaneous manifestations of underlying medical conditions. In addition to medical causes of both psychiatric and dermatological conditions there are two addition important areas of overlap.

The first are conditions where there is no known clear unitary pathophysiology.  On the dermatology side there are many common and rare conditions like atopic dermatitis, seborrheic dermatitis, acne vulgaris, rosacea, psoriasis, granuloma annulare, vitiligo, lichen planus, erythema multiforme, bullous pemphigoid, and pemphigus vulgaris. Many have one or more hypotheses about the pathophysiology, and these hypotheses guide treatment attempts. The case report above is a clear example of multiple treatments contained in the same topical medication for rosacea. The major psychiatric disorders when underlying medical causes have been ruled out are in a similar situation.  Over the past 30 years there have been over 100 hypotheses about the pathophysiology of depression and recently (21) some of these hypotheses have been combined.

The second are conditions where there is overlap between psychiatry and dermatology sometimes called psychodermatology.  A study by Balieva, et al (19) examined the bidirectional relationship between skin disorders and psychiatric disorders.  It was a large registry study from Norway that selected patients based on their seeing dermatologists and being treated for common disorders with psychiatric disorders being the outcome variable.  That population was compared to a non-dermatology diagnosis groups and odds rations were calculated. The authors demonstrated that patients were 2-3 times as likely to develop depression given the dermatology diagnoses with elevated risks for anxiety, somatoform disorders, and obsessive-compulsive disorder but not eating disorders. The authors reconciled this with several previous studies with similar findings, but they broadened the number of psychiatric diagnoses.

Psychodermatology classifies the combination of psychiatric and dermatological disorders based on which disease is primary and whether the skin pathology is a manifestation of psychopathology (delusional parasitosis, trichotillomania, pathological skin picking, and psychogenic pruritis.).  In a recent study (17), the latter group had a very high risk of neuropsychiatric disorders – depression, anxiety disorders, and personality disorders.  Dermatology conditions exacerbated by stress including atopic dermatitis, psoriasis, acne vulgaris, and vulvodynia – were all associated with depressive disorders, sleep disorders, and neurodevelopmental disorders.

7:  Mild-moderate-severe designations:  Another common criticism of psychiatric diagnoses is that it seems like the following qualifier in most diagnostic criteria is arbitrary:

The disturbance causes clinically significant distress or impairment in social, occupational, or other important areas of functioning.  

Since there are no formal diagnostic criteria for dermatology there is no threshold for diagnosis.  That is not necessarily problematic since the many intermediate phenotypes for any major psychiatric disorder may also not meet this threshold but nonetheless seem like a good idea to treat.  In dermatology practice there is a significant cosmetic component that can be purely subjective.  Many studies have mild-moderate-severe categorizations based on the judgment of clinicians.  In dermatology, the disappearance of the cutaneous manifestations often leads to the patient stopping treatment when the treatment needs to be continued.  An example is the use of emollients in atopic dermatitis and avoiding skin irritants.  

8: Medications with clearly defined mechanism of action: Glucocorticoids (prednisone, triamcinolone, betamethasone) have potent effects on inflammatory and immune responses that are not disease specific.  Biologics for dermatology conditions can be pathway specific for inflammatory pathways, but they are not technically disease specific.

I reviewed 11 monoclonal antibodies used for dermatology diseases and found that they are characterized as pathway specific but not disease specific.  In other words, they shut down specific inflammatory pathways that can be involved in more than one dermatological disease and there is overlap with other allergic, rheumatic, and inflammatory diseases as well as cancer.  



On  larger scale,  the overlap between immune medicated conditions obviously cuts across the turf of multiple specialties.  That includes several CNS diseases that produce neuropsychiatric syndromes and may soon include purely psychiatric disorders with no other identifiable causes (22).



9: Transdiagnostic considerations:  there has been and explosion of the use of the term “transdiagnostic” in psychiatry – typically as a criticism to suggest that diagnostic categories are cruder than in the rest of medicine. The term is just being extended to other commentaries.  In the case of dermatology – rash is considered one of the leading symptoms that leads to medical evaluations. Here is a list of 71 causes of a rash.  But the transdiagnostic concept does not stop there it also applies to treatments across several categories that are non-specific but effective. 

Transdiagnostic was initially considered based on the assumptions that disorders had similar etiological and maintenance factors and responded to non-specific therapies like cognitive behavioral therapy (16).  One of the associated criticisms was that categorical diagnosis was less specific due to high comorbidity.  In a systematic review of 116 studies only 3% met standardized criteria (Mansell) for a study of transdiagnostic approaches in psychiatry (16). That same study reviewed all the commonly used approaches.  The authors conclude that transdiagnostic approaches have overpromised and undelivered.  The authors present an extensive discussion of the problems some of which are evident in a typical network diagram they include in their paper.  I want to focus on just one – and that is: “transdiagnostic approaches are largely based on an epistemological error, which triggers an illusion of continuity”.  When classification systems like the DSM and ICD use simplified language – psychopathology is ignored and it collapses reality into unitary disorders.  Suddenly all depression is equivalent to a handful of criteria.  A depression checklist like the PHQ-9 suddenly becomes a phenotype for large epidemiological studies and a basis for transdiagnostic treatments.

What happened to the endophenotypes of the recent past?  If there is any variance in clinical practice it all seems to be swept into a spectrum or a continuum like electromagnetic radiation. The real patterns that physicians have diagnosed and treated with success over the years are collapsed into a number on rating scale and there are as many rating scales as you want.

The dermatology diagnoses discussed so far are clear reasons why we need to keep the patterns real. Recognizing the importance of those patterns is why there is no PHQ-9 for atopic dermatitis.  This process has often been oversimplified as prototypical diagnoses in the past – but there are no protypes when you are recognizing hundreds of different microphenotypes rather than one large oversimplified one.               

10:  Polypharmacy, cure, and discontinuation:  Deprescribing has become the latest buzzword used to criticize psychiatry as if psychiatrists have never discontinued medications in the past and do not know how to do it.  As far as I know this has not be a problem focused on dermatology, but many papers say that patients frequently stop their medications prematurely because they are worried about using them on a long-term basis.  With all complex polygenic illnesses – being followed by a physician familiar with your problem who can monitor the course of the illness and make the appropriate adjustments is the best course.  That is necessary because most of these diseases are genetically complex and not predictable. Detrimental genotypes may never be expressed or in the case presented occur in old age rather than youth.  Environmental factors are also important.  Physicians are all generally trained to do that monitoring and decide when the medications can be stopped or held.  In the case where a maintenance medication is needed, they also have a goal of minimizing side effects from it.

I am hoping that the above comparisons make sense. Much of the hyperbole focused on psychiatry is not based in how psychiatry is taught or practiced.  Psychiatry is often isolated from the rest of medicine when it uses the same diagnostic and treatment approach.  It has the same genetic architecture as other polygenic diseases.  Even though the DSM has criteria listed for diagnoses – a diagnosis by a psychiatrist is much more than that.  Just like a dermatologist can see several diagnostic equivalents rashes, a psychiatrist is able to recognize many phenotypes of illness that are equivalent to the classification.  Those phenotypes include both validity markers and psychosocial characteristics that are not listed in the DSM but are important for individualized care.  And contrary to what you might read – it does not take an extensive battery of testing to get results.  

 

George Dawson, MD, DFAPA

 

Supplementary 1:

In human embryology the skin and the brain both originate form the same germ layer - the ectoderm.  The ectoderm differentiates into the neuroectoderm  and surface ectoderm that eventually becomes the epidermis and the surface appendages (hair and nails)  

Supplementary 2:

A typical polygenic risk analysis is available at the top of this post:  https://real-psychiatry.blogspot.com/2024/04/what-economist-doesnt-know-about.html

Note that this patient is at risk for 9 dermatology and 9 psychiatric conditions according to this graph.


Graphics Credit:

 Lead graphic is from:  

Boguniewicz, M, Fonacier L, Leung DYM. Atopic and contact dermatitis. In: Rich, Robert R., Fleisher, Thomas A, Shearer, William T., Schroeder, Harry, Frew, Anthony J., Weyand, Cornelia M.  Clinical Immunology : Principles and Practice, 5th ed. London: Elsevier; 2018  : p. 614

License number:  1693945-1

Graphics 2 and 3 were generated by me from FDA package inserts in Graphic 2 and the Table of Contents of the leadg graphics text (Rich R, Shearer TA, et al) and several research papers in the case of Graphic 3.  


References:

 1:  World Health Organization.  WHO’s first global meeting on skin NTDs calls for greater efforts to address their burden.  March 31, 2023:  https://www.who.int/news/item/31-03-2023-who-first-global-meeting-on-skin-ntds-calls-for-greater-efforts-to-address-their-burden

2:  Grada A, Muddasani S, Fleischer AB Jr, Feldman SR, Peck GM. Trends in Office Visits for the Five Most Common Skin Diseases in the United States. J Clin Aesthet Dermatol. 2022 May;15(5):E82-E86. PMID: 35642232; PMCID: PMC9122273.

3:  Schaefer I, Rustenbach SJ, Zimmer L, Augustin M. Prevalence of skin diseases in a cohort of 48,665 employees in Germany. Dermatology. 2008;217(2):169-72. doi: 10.1159/000136656. Epub 2008 Jun 5. PMID: 18525204.

4:  Schäfer T. Epidemiology of psoriasis. Review and the German perspective. Dermatology. 2006;212(4):327-37. doi: 10.1159/000092283. PMID: 16707882.

5:  Tian J, Zhang D, Yang Y, Huang Y, Wang L, Yao X, Lu Q. Global epidemiology of atopic dermatitis: a comprehensive systematic analysis and modelling study. Br J Dermatol. 2023 Dec 20;190(1):55-61. doi: 10.1093/bjd/ljad339. PMID: 37705227.

6:  Laughter MR, Maymone MBC, Mashayekhi S, Arents BWM, Karimkhani C, Langan SM, Dellavalle RP, Flohr C. The global burden of atopic dermatitis: lessons from the Global Burden of Disease Study 1990-2017. Br J Dermatol. 2021 Feb;184(2):304-309. doi: 10.1111/bjd.19580. Epub 2020 Nov 29. PMID: 33006135.

7:  Gether L, Overgaard LK, Egeberg A, Thyssen JP. Incidence and prevalence of rosacea: a systematic review and meta-analysis. Br J Dermatol. 2018 Aug;179(2):282-289. doi: 10.1111/bjd.16481. Epub 2018 May 31. PMID: 29478264

8:  Polaskey MT, Chang CH, Daftary K, Fakhraie S, Miller CH, Chovatiya R. The Global Prevalence of Seborrheic Dermatitis: A Systematic Review and Meta-Analysis. JAMA Dermatol. 2024 Aug 1;160(8):846-855. doi: 10.1001/jamadermatol.2024.1987. PMID: 38958996; PMCID: PMC11223058.

9:  Skayem C, Richard MA, Saint Aroman M, Merhand S, Ben Hayoun Y, Baissac C, Halioua B, Taieb C, Staumont-Sallé D. Epidemiology of atopic dermatitis: a global worldwide study. Clin Exp Dermatol. 2025 Sep 25;50(10):2054-2056. doi: 10.1093/ced/llaf233. PMID: 40448692.

10:  Urban K, Chu S, Scheufele C, Giesey RL, Mehrmal S, Uppal P, Delost GR. The global, regional, and national burden of fungal skin diseases in 195 countries and territories: A cross-sectional analysis from the Global Burden of Disease Study 2017. JAAD Int. 2020 Nov 30;2:22-27. doi: 10.1016/j.jdin.2020.10.003. PMID: 34409349

11:  Lazanas P, Antonatos C, Tsoumani KT, Sgourou A, Vasilopoulos Y. Shared Genetic Architecture Between Atopic Dermatitis and Autoimmune Diseases. Int J Mol Sci. 2025 Sep 18;26(18):9124. doi: 10.3390/ijms26189124. PMID: 41009683; PMCID: PMC12470386.

12:  Richard MA, Paul C, Nijsten T, Gisondi P, Salavastru C, Taieb C, Trakatelli M, Puig L, Stratigos A; EADV burden of skin diseases project team. Prevalence of most common skin diseases in Europe: a population-based study. J Eur Acad Dermatol Venereol. 2022 Jul;36(7):1088-1096. doi: 10.1111/jdv.18050. Epub 2022 Mar 22. PMID: 35274366; PMCID: PMC9415115.

13:  Norman GR, Rosenthal D, Brooks LR, Allen SW, Muzzin LJ. The Development of Expertise in Dermatology. Arch Dermatol. 1989;125(8):1063–1068. doi:10.1001/archderm.1989.01670200039005

14:  Tran H, Chen K, Lim AC, Jabbour J, Shumack S. Assessing diagnostic skill in dermatology: a comparison between general practitioners and dermatologists. Australas J Dermatol. 2005 Nov;46(4):230-4. doi: 10.1111/j.1440-0960.2005.00189.x. PMID: 16197420.

15:  Aljohani AG, Abduljabbar MH, Hariri J, Zimmo BS, Magboul MA, Aleissa SM, Baabdullah A, Alqutub A, Alafif K, Faidah H. Assessing the Ability of Non-dermatology Physicians to Recognize Urgent Skin Diseases. Cureus. 2023 Apr 19;15(4):e37823. doi: 10.7759/cureus.37823. PMID: 37214029; PMCID: PMC10197985.

16:  Fusar-Poli P, Solmi M, Brondino N, Davies C, Chae C, Politi P, Borgwardt S, Lawrie SM, Parnas J, McGuire P. Transdiagnostic psychiatry: a systematic review. World Psychiatry. 2019 Jun;18(2):192-207. doi: 10.1002/wps.20631. PMID: 31059629; PMCID: PMC6502428.

17:  Abdi P, Turk T, Haq Z, Diaz MJ, Dytoc M. Epidemiology and Comorbidities of Psychodermatologic Conditions. J Cutan Med Surg. 2025 Jun 24:12034754251347569. doi: 10.1177/12034754251347569. Epub ahead of print. PMID: 40552522.

18:  Van Beugen S, Schut C, Kupfer J, et al. Perceived Stigmatization among Dermatological Outpatients Compared with Controls: An Observational Multicentre Study in 17 European Countries. Acta Derm Venereol. 2023 Jun 22;103:adv6485. doi: 10.2340/actadv.v103.6485. PMID: 37345973; PMCID: PMC10296546.

19:  Balieva F, Abebe DS, Dalgard FJ, Lien L. Risk of developing psychiatric disease among adult patients with skin disease: A 9-year national register follow-up study in Norway. Skin Health Dis. 2023 Oct 20;3(6):e294. doi: 10.1002/ski2.294. PMID: 38047256; PMCID: PMC10690693.

20:  Farage MA. The Prevalence of Sensitive Skin. Front Med (Lausanne). 2019 May 17;6:98. doi: 10.3389/fmed.2019.00098. PMID: 31157225; PMCID: PMC6533878.

21:  Baumberger B, Batey L, Hashemi P. How three different theories of depression converge at inflammation. Discov Ment Health. 2025 Dec 24;5(1):197. doi: 10.1007/s44192-025-00312-4. PMID: 41442021; PMCID: PMC12738436.

22:  Rizk MM, Bolton L, Cathomas F, He H, Russo SJ, Guttman-Yassky E, Mann JJ, Murrough J. Immune-Targeted Therapies for Depression: Current Evidence for Antidepressant Effects of Monoclonal Antibodies. J Clin Psychiatry. 2024 Jun 24;85(3):23nr15243. doi: 10.4088/JCP.23nr15243. PMID: 38959503; PMCID: PMC11892342.

23:  Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Electronic address: andrew.mcintosh@ed.ac.uk; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies. Cell. 2025 Feb 6;188(3):640-652.e9. doi: 10.1016/j.cell.2024.12.002. Epub 2025 Jan 14. PMID: 39814019; PMCID: PMC11829167

24: Mellerup E, Møller GL. Combinations of Genetic Variants Occurring Exclusively in Patients. Comput Struct Biotechnol J. 2017 Mar 10;15:286-289. doi: 10.1016/j.csbj.2017.03.001. PMID: 28377798; PMCID: PMC5367802. 


Friday, January 16, 2026

How To Address the Opioid Crisis Without Gunboat Diplomacy


 

The United States is currently suffering through the self-inflicted crisis of electing an authoritarian administration.  Civil, legal, international, and diplomatic standards are routinely ignored and there seems to be no clear overriding strategy other than promulgating the autocracy.   271,000 government employees were fired, many leading experts in fields that ran counter to the autocratic myths, but most were loyal career government employees dedicated to serve the public.  USAID – a government agency providing technical assistance and medical care in the developing world since 1961 was officially shut down by the Trump administration in 2025.  USAID was credited with saving 92 million lives over 20 years and there is currently a tracker in place that estimates how many people will die as a result of the closure.  It is clear the Trump administration is not afraid to be directly responsible for the deaths of tens of thousands if not millions of people.  It might fit their American first narrative if there was any possible benefit to Americans.  Their political calculus apparently ignores any goodwill effects of helping people and a national image unlike the usual totalitarian regimes.

Trump’s version of the War on Drugs, is smaller in scale but probably much more deadly.  Trump began sinking boats in the Caribbean and the Pacific Ocean that he alleged were carrying drugs in September 2025.  Since then, 35 vessels were struck or sunk killing 123 people (1).  There is ongoing debate that this is not standard drug interdiction where drug smugglers are stopped by the Coast Guard, arrested, and stand trial.  This is unlawful killing of suspected drug smugglers where the public is supposed to accept that the alleged intelligence about whether they were smuggling drugs is accurate.  Given this administration’s honesty track record there is no way that should be accepted at face value.  The Coast Guard’s own statistics suggests that 25% of the vessels they board for the same reason are not carrying drugs. Even if they were this is essentially executing people without due process for what are non-capital crimes in the US.

In addition to the strikes that are seen as criminal there have been 2 incidents suggesting additional war crimes.  In the first, survivors of the first strike were killed while they were swimming in the water.  In the second, a strike was made by a plane disguised as a civilian plane.  According to the Geneva Convention it is a war crime to feign protected status and then attempt to kill, capture, or injure an adversary.  This is known as perfidy and it is accepted by the US.  Like most activity from this administration there appears to be no clear rationale for all this activity and despite the outcry over potential war crimes – the actions continue unabated.

I had plenty of experience teaching diverse groups of students about the opioid epidemic from about 2008 to 2020.   Overdose deaths were a proxy for the epidemic.  The initial part of that curve was due to excessive prescribing that was associated with a pain as the fifth vital sign initiative.  There were excessive and escalating prescriptions for the treatment of chronic pain.  In many cases there were diversions of these drug supplies for non-prescription use.  In some cases, there were pill mills that specialized in writing opioid prescriptions in great numbers.  They resembled medical practices from the turn of the 20th century that maintained people in addiction rather than treating any specific medical problems. As more prescription opioids were diverted it was more cost effective to purchase heroin and that was the first significant change in consumed opioid composition.  By 2014 synthetic opioids (fentanyl and carfentanyl)  were introduced into this landscape.  They had much higher potency than the usual prescription opioids and that led to more drug overdoses.  Countermeasures were introduced including intranasal naloxone and fentanyl test kits.  These are effective measures if available but there is some misunderstanding by the public, who sees opioid users as risk averse.  They are not risk averse but both countermeasures are useful in saving lives.  The most effective life saving measure is MOUD or medication for opioid use disorder including buprenorphine, methadone, and naltrexone.

That brings me to a paper on the sudden reduction in overdose deaths beginning in mid-2023 and extending into 2025 in both the US and Canada.  During that time the overdose death rates dropped by 30%(3).  The graph of that reduction is shown below.  The data is a combination of confirmed data by the CDC and provisional counts and projections by NCHS for the last two years.  It is very similar to the graphs generated by the authors in their report.

 


 The authors used a novel approach to investigate the idea that supply shock accounts for the decrease in overdose deaths.  During treatment it is common for people using non-prescription controlled substances to report shortages and changes in the drug supply as well as price changes to their treatment providers.  The authors used the number of drug seizures and opioid concentration estimates of those seizures to estimate fentanyl shortages.  They also searched a popular social media site (Reddit) for any mention of the term fentanyl or a reasonable facsimile and drought or shortage or a facsimile.  The Reddit data was compromised from January to July of 2024 by moderation on the basis that these posts about fentanyl shortages violated terms of use.

Both groups of indicators of decreased drug supply (drug seizures and concentration and social media posts indicating a drug shortage) correlated with a drop in drug overdose deaths.  By the end of 2024 the opioid overdose death rates and concentrations has fallen by about 30% (see graph).  The original paper plots both fentanyl concentration in pills and powders as well as death rates).

What caused these drops?  The authors suggest several possibilities.  First, China acted against the manufacture and sale of fentanyl and the precursors starting in late 2023. That included online platforms.  There was a meeting between President Biden and Xi in November 2023 that focused on law enforcement cooperation in this area.  The authors suggest this is a low-cost opportunity for China to get leverage in negotiations with the US. Second, US drug interventions in Mexico may have reduced the supply, but the authors point out that the DEA would have paradoxically claimed no credit in their reporting but did credit the lack of precursors from China.  These factors are strong arguments for supply chain disruption as a cause of the drop in opioid overdose deaths.  

Could this be a generational effect?  From a demographic standpoint, the 25-plus year opioid epidemic represents an entire generation.  The succeeding generation has seen the toll of these drugs as both mortality and morbidity.  The younger generation has been noted to have some sober initiatives and they are consuming less alcohol than their predecessors. Is it possible that this subcultural movement is extended to other intoxicants?  The 0-25 year age group typically has the lowest number of overdose deaths and by itself is unlikely to account for the decrease.

The authors suggest three implications of their paper.  First, that dealer level interdiction on the street may not be necessary to reduce drug trafficking.  Presumably that intervention has been constant over the course of the recorded data and it has had minimal impact. It is also high risk and places certain demographic groups at higher risk.  Second, this may be a transient effect.  Drug traffickers can adapt as noted by the difference in how fentanyl makes it to the streets in the US compared to Canada.  Finally, if this was in fact the effect of diplomacy those efforts should be increased rather than decreased. India is a source of precursors and in some cases direct to consumer shipments of controlled substances.  Diplomatic efforts could yield further disruptions in the supply chain and less problems with opioids on the streets of the US.       

There is now a fourth implication and that is that gunboat diplomacy and the questionable use of the US military against boats suspected of carrying drugs is unnecessary.  It also highlights that the Executive order to declare war on cartels was also unnecessary.  There was a substantial drop in overdose deaths before any of these measures was employed and it may be an indication that diplomacy is the way forward.  If I was a conspiracy theorist, I would suggest that Trump wants to get credit for the drop in fentanyl trafficking that occurred in the Biden administration.  According to the authors of this paper we do not know if this is a long-term trend or not. Since most fentanyl trafficking does not originate in Venezuela or the countries surrounding the targeted areas it is unlikely to have a big effect.  

That 30% decrease in mortality cannot be ignored and a closer examination of the Biden negotiations is warranted.

 


 

 

George Dawson, MD, DFAPA


References:

1:  Watson B, Peniston B.  The D Brief: ‘Perfidy’ in boat strike?; Pentagon’s new AI plan; Venezuela’s broken air defenses; Quantum space cameras; And a bit more.  Defense One.  January 13, 2026.  https://www.defenseone.com/threats/2026/01/the-d-brief-january-13-2026/410643/

2:  Tait R. Killing of survivors sparks outrage – but entire US ‘drug boat’ war is legally shaky.  The Guardian.  December 4, 2025.  https://www.theguardian.com/us-news/2025/dec/04/venezuela-boat-strikes-legality-hegseth

3:  Vangelov K, Humphreys K, Caulkins JP, Pollack H, Pardo B, Reuter P. Did the illicit fentanyl trade experience a supply shock? Science. 2026 Jan 8;391(6781):134-136. doi: 10.1126/science.aea6130. Epub 2026 Jan 8. PMID: 41505547.

Graphics:

1:  Lead graphic is from the CDC and is in the public domain: https://www.cdc.gov/overdose-prevention/about/understanding-the-opioid-overdose-epidemic.html

2:  The graph of fatal overdose deaths per year shows the number of fatal opioid overdose deaths in the United States from 2000 to 2025.  The data from 2000 to 2023 consists of final and reported figures from the Centers for Disease Control and Prevention (CDC) and the National Institute on Drug Abuse (NIDA). The figures for 2024 and 2025 are based on provisional counts and projections released by the CDC's National Center for Health Statistics (NCHS) as of January 2026.

3:  Graphics 2 and 3 were designed with the assistance of Google Gemini.

Saturday, January 10, 2026

The Problems With AI Are More Readily Apparent

 



Note:  This essay is written by an old human brain that was writing essays and poetry decades before there was an Internet. No AI was used to create this essay.

Artificial Intelligence (AI) hype permeates every aspect of modern life.  We see daily predictions of what group of workers will be replaced and how AI is going to cure every human disease. It is no accident that the main promoters of AI will make significant profits from it.  Vast amounts of money are being invested and gambled on AI on Wall Street.  Educators are concerned that students are using it to write the essays that took us hours or days to write in college – in just a few minutes.  That application leads to the obvious questions about what will be the end product of college if all the serious, critical, and creative thought has been relegated to a machine. 

Apart from sheer data scraping and synthesis of what amounts to search inquires - AI seems to be imbued with magical qualities that probably do not exist. It is like the science fiction of the 20th century – alien beings superior to humans in every way because they lack that well know weakness – emotion.  If only we had a purely rational process life would be much better.  The current promoters tend to describe this collective AI as making life better for all of us and minimize any risks.  The suggested risks also come from the sci-fi genre in the form of Terminator type movies where the machines decide it is in their best interest to eliminate humans and run the planet on their own. There are the usual failed programs to preserve human life at all costs or to destroy humans only if they are carrying weapons. 

But AI thought experiments do not require even that level of complexity to create massive problems.  Consider Bostrom’s well known example of a paper clip making machine run by AI (1).  In that example PaperClip AI is charged with the task of maximizing paperclip production.  In a case of infrastructure profusion it “proceeds by converting the Earth and increasingly large chunks of the observable universe into paperclips.”   He gives several reasons why obvious fixes like setting a production limit or a production interval would probably not work and leads to infrastructure profusion that would be catastrophic.  The current limitation on this kind of AI is that it does not have control over acquiring all these resources.  It also lacks the ability to perceive how correct production in an fully autonomous mode.   Bostrom also adds characteristics to the AI – like motivation and reinforcement that seem to go beyond the usual conceptualizations.  Where would they come from?  If we are not thinking about programmed algorithms what kind of intelligence has its own built-in reinforcement and motivation schedule independent of the environment?  After all – the task of producing just enough paperclips without consuming all of the resources on the planet is an easy enough task for a human manager to accomplish.  Bostrom suggests that it is an intuitive task for humans but not so much for machines.

A couple of events came to my attention in the past week that make the limitations of current AI even more obvious – especially contrasted with the hype.  The first is the case of a high-profile celebrity who has lodged a complaint against the X(formerly Twitter) AI called Grok.  In it, she points out that the AI has been generating nude or semi-nude photos of her adult and teen-age photos. I heard an interview where she mentions that this practice is widespread and that other women have contacted her about the same problem.  This practice is in direct contrast with the X site use policy saying that users doing this will be banned and referred for prosecution. She has not been successful in getting the photos stopped and removed.

The second event was a Bill Gates clip where he points out that what he considers a sensitive measure of progress – mortality in children less than 5 years of age - has taken a turn for the worse.  He predicts the world descending into a Dark Age if we are not able to reverse this change. That new release comes in the context of Gates predicting that AI will replace physicians, teachers, and most humans in the workplace in the next 10 years.  Of course he was promoting a book at the time.  In that same clip he was optimistic about the effects of AI on health and the climate despite the massive toll that AI creates on power generating resources to the point that some companies are building their own municipal sized power plants. 

What are the obvious disconnects in these cases?  In the first, AI clearly has no inherent moral decision making at this point.  That function is still relegated to humans and given what is being described here that is far from perfect.  In this case the complainant has some knowledge of the social media industry and said that she thought that any engineer could correct this problem quickly.  I am not a computer engineer so I am speculating that would take a restrictive or algorithmic program. But what about the true deficit here?  It could easily be seen as a basic deficit in empathy and an inability to apply moral judgment and its determinants to what are basic human questions.  Should anyone be displaying nude photos of you without your consent?  Should identified nude photos of children ever be displayed?  AI in its current iteration on X is clearly not able to answer these questions in an acceptable way and act accordingly.

The second contrast is only slightly more subtle. Conflict of interest is obvious but Gates seems to not recognize his described descent into the Dark Ages based on an increasing death rate in children 5 years of age and younger depends almost entirely on human decision making.   It runs counter to the decades of medical human decision making that he suggests will be replaced. Basic inexpensive life-saving medical care has been eliminated by the Trump administration.  This has led to the predictions that hundreds of thousands if not millions of people will die as a direct result. Is AI going to replace politicians?  What would be the result if it did?  Cancelling all these humanitarian programs is a more complicated decision than not publishing nude photos of non-consenting adults or any children.  It is a marginally rational ideological decision.  Is the AI of different politicians going to reflect their marginally rational ideology or are we supposed to trust this political decision to a machine with unknown biases or ideologies?  How will that AI decision making be optimized for moral and political decision making?  Will AI be able to shut down the longstanding human tendency to base decisions on power over morality?  If politicians allow AI to replace large numbers of workers, will it also be able to replace large numbers of politicians and managers?  It can easily be argued that the decisions of knowledge workers are more complex than that of managers.                                    

A key human factor is empathy and it requires emotional experience.  You get a hint of that in the best technical description of empathy I have seen from Sims (2):

“Empathy is achieved by precise, insightful, persistent, and knowledgeable questioning until the doctor is able to give an account of the patient’s subjective experience that the patient recognizes as his own… Throughout the process, success depends upon the capacity of the doctor as a human being to experience something like the internal experience of the other person, the patient: it is not an assessment that could be carried out by a microphone and a computer.  It depends absolutely upon the shared capacity of both the doctor and patient for human experience and feeling.”  (p. 3)

The basic problem that machines have is that they are not conscious at the most basic level.  They have no experience.  In consciousness research, early thinking was that a machine would be conscious if a human communicating with it experienced it like another human being.  That was called the Turing Test after the scientist who proposed it.  In the case of computerized chess – there was a time several years ago when the machine was experienced like it was making the chess moves of a human being.  The headlines asked “has the Turing Test been passed?” It turns out the test was far too easy.  There are after all a finite number of chess moves and plenty of data about the probabilities of each move made by top players. That can all be handled by number crunching.

What happens when it comes to real human decisions that require the experience?  And by experience I mean the event with all of the integrated emotions.  Is AI likely to recognize the horror of finding your nude photos on the Internet,  or scammers trying to blackmail you over a fictional event, or the severity of your anxiety from being harassed at work, or the devastating thoughts associated with genocide or nuclear war?  Machines have no conscious experience.  Without that experience how can we expect a machine to understand why the sexual exploitation of children and adults is immoral, wrong, or even anxiety producing?

It is also naïve to think that AI will produce ideal decisions.  Today’s iteration may be the crudest form but everyone is aware of the hallucinations. The more correct term from psychiatry is confabulation or making things up as a response to a specific question.  When you consider that today’s AI is mostly a more sophisticated search engine there really is no reason for it.  As an example, I have asked for an academic reference in a certain citation style and will get it.  When I research that reference – I find that it does not exist.  I have had to expend considerable time finding the original journal and looking for the reference in that edition to confirm it is non-existent.  Explanations for these phenomena extend to poor data quality, poor models, bad prompts, and flawed design.  The problem is acknowledged and many AI sites warn about the hallucinations.  A more subtle problem at this point is how AI will be manipulated by whatever business, government, or political body that controls it. That problem was pointed out in a book written about a decade ago (3) illustrating how algorithms applied to individual data can reinforce human biases about race and poverty and promote inequality. I have seen no good explanations about why AI would be any different and in fact it probably makes the financial system less secure.

As I keep posting about how your brain and mind work – please keep in mind it is a very sophisticated and complex process. It is much more than looking at every available reference and synthesizing an answer.  There are the required experiential, emotional, cognitive, value-based, and moral components.  Superintelligence these days implies that at some point machines will always have the correct and best answer.  That certainly does not exist now and I have a question about whether it will in the future. It is a good time to take a more realistic view of AI and construct some guardrails.     

      

George Dawson, MD, DFAPA

 

 

References:

1:  Bostrom N.  Superintelligence: Paths, Dangers, Strategies.  Oxford, England: Oxford University Press, 2014: 150-152. 

2:  Sims A.  Symptoms in the Mind: An Introduction to Descriptive Pathology.  London, England: Elsevier Limited, 2003: 3.

3:  O’Neil C.  Weapons of Math Destruction. New York City, USA; Crown Books, 2016